http://si410wiki.sites.uofmhosting.net/api.php?action=feedcontributions&user=WikiSysop&feedformat=atomSI410 - User contributions [en]2024-03-28T16:55:17ZUser contributionsMediaWiki 1.25.2http://si410wiki.sites.uofmhosting.net/index.php?title=Ancestry_data&diff=91566Ancestry data2021-02-12T09:12:58Z<p>WikiSysop: Undo revision 91528 by WikiSysop (talk)</p>
<hr />
<div>{{Nav-Bar|Topics##}}<br><br />
<br />
[[File:Britishfamilytree.png|400px|thumb|right|The current family tree of British Royalty]]<br />
'''Ancestry data''' is collected information tracking an individual's [[Wikipedia:Lineage_(genetic)|family lineage]] to identify their [[Wikipedia:Ethnic_origin|ethnic origin]], heritage, [[Wikipedia:Place_of_birth|place of birth]], and relatives. For thousands of years, records of [[Wikipedia:ancestor|ancestry]] data have been kept to clearly define concepts of birthright and familial succession throughout both modern and ancient civilizations.<br />
<br />
Modern day technology has changed the processes of collecting and interpreting ancestry data. Companies like [https://www.23andme.com/ 23andMe] and [https://www.ancestrydna.com/kits/?s_kwcid=ancestors+dna&gclid=Cj0KCQjw1pblBRDSARIsACfUG13YNht7Foyirz_we6B_loNzZBh9I8RnQOcmxOL1a5TtUboVHYU7fgQaAikDEALw_wcB&gclsrc=aw.ds&o_xid=79108&o_lid=79108&o_sch=Paid+Search+Non+Brand AncestryDNA] offer full ethnic background maps and potential [[Wikipedia:family_tree|family tree]] links to the general public. This thriving [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer (DTC)] ancestry data industry originates from a fascination with the concept of human identity and origin. The industry's success has lead to a new form of personalized medicines and treatment plans based on genetic makeup. As of 2014, [[Wikipedia:Genealogy|genealogy]] was a 2-billion-dollar industry, and is continuing to grow.<ref> “The Genealogy Industry: $2 Billion–and Growing!” Genealogy Gems, 11 Dec. 2014, [http://lisalouisecooke.com/2014/12/11/genealogy-industry-growing lisalouisecooke.com/2014/12/11/genealogy-industry-growing]/.</ref> These new companies have made it affordable and efficient for the general public to discover more about themselves in terms of ancestry. However, as ancestry data is applied to health care solutions, a range of ethical problems, including racial supremacy, health, and privacy, emerges. <br />
<br />
== Modern Day Use and Influence of Technology ==<br />
Since the discovery of the [[Wikipedia:Nucleic_acid_double_helix|double helix]] by [[Wikipedia:Francis_Crick|Francis Crick]] and [[Wikipedia:James_Watson|James Watson]] in 1935, scientists have worked tirelessly to better understand DNA, the [[Wikipedia:human_genome|human genome]] and its countless implications. The [[Wikipedia:Human_Genome_Project|Human Genome Project]], the first full human DNA sequence in history, cost almost $3 billion.<ref>"The Human Genome Project Completion: Frequently Asked Questions" https://www.genome.gov/11006943/human-genome-project-completion-frequently-asked-questions/</ref> In contrast with today's technological advancement, an individual can get their DNA sequenced and analyzed for personal use through a variety of popular vendors for under $100. <br />
<br />
=== How Ancestry Data Works Today ===<br />
[[File:23.jpg|1000px|thumb|left|23andMe DNA testing kit, along with its instructions]]<br />
With the popularization of discovering familial lineage online, it has become incredibly simple for a person to receive their ancestry data. Interested parties simply purchase a kit from a direct-to-consumer DNA testing company, such as [[Wikipedia:23andMe|23andMe]] or any of the other 25 major competitors, follow the [[Wikipedia:DNA|DNA]] harvesting directions (i.e. spit into a small plastic tube), and send the completed test kit back to the company. Once a DNA sample is received, the company will process and sequence the DNA. In the case of 23andMe, this process takes 3-5 weeks.<ref> "When Will My Results Be Ready?" 23andMe, https://customercare.23andme.com/hc/en-us/articles/202904740-When-will-my-results-be-ready- </ref> With the help of trained professionals and [[algorithms]], DNA sequencing can provide information that changes lives. Robin Smith, head of 23andMe’s Ancestry program explains how the algorithm works: "it takes an entire genome and chunks it up...It takes little pieces, and for each piece, it compares it against the reference data set. It compares it against British; it compares it against West African; it goes through the entire list, and it spits out a probability for [where that piece of DNA came from]" <ref>Letzter, Rafi. “How Do DNA Ancestry Tests Really Work?” LiveScience, Purch, 4 June 2018, [http://www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html].</ref>. DNA sequencing also has the capability to speculate potential relatives based on near complete matches of DNA, and boasts very accurate results for those considered “Close Family” or “First Cousins”. <br />
<br />
Overall, technological advancements have made it relatively easy for the general public to access their genetic identity. With few barriers to entry, a relatively low cost, and a quick turnaround, access to ancestral data is quite easy. However, the modern applications of genetic data have become far more contentious when considering the ethical implications of a large quantity of ancestral data.<br />
<br />
===Genealogy Companies===<br />
There are many different [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer personal genomic websites] that allow individuals to receive a report of their ancestry data. Distinguishable characteristics among these genealogy databases aside, the sheer variety in platforms and the user base they each hold demonstrates the true prevalence of ancestry data in modern life. Different sites use different reference databases to compare your genetic information. Thus, results may differ between different companies from the same genetic information.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Company !! Description<br />
|-<br />
| [https://www.ancestry.com/ Ancestry] || One of the most popular direct-to-consumer personal genomic websites with 3 million paying members that offers access to 10 billion historical reports<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com. en.wikipedia.org/wiki/Ancestry.com]"</ref>.<br />
|-<br />
| [https://www.23andme.com/?mdc1=true 23 and Me] || A DNA test kit service that provides over 125 reports in an individual's ancestry, health predispositions, wellness, carrier status, and traits <ref>23andMe. “DNA Genetic Testing &amp; Analysis.” 23andMe, [https://www.23andme.com/23andMe]</ref>.<br />
|-<br />
| [https://www.familysearch.org/en/ Family Search] || Free of charge genealogy database service that offers comprehensive sequencing and analysis of personal DNA.<br />
|-<br />
| [https://www.myheritage.com/?utm_source=ppc_google&utm_medium=cpc&utm_campaign=mh_search_us_en_des_mul_exact_myheritage&utm_content=289683747520&utm_term=my+heritage&tr_camp_id=344023924&tr_ad_group=myheritage&tr_ag_id=24241928044&tr_placement=&tr_device=c&tr_account=904-055-9108&keyword=&tr_size=&recordtype=&recordlocation=&gclid=EAIaIQobChMIz-_TxKzm4QIVgbbACh1Qag_hEAAYASAAEgKONPD_BwE My Heritage] || Israel-based company that provides a DNA test kit service that is delivered to consumers' homes and processed quicker than any other service option. The company supports 92 million users worldwide<ref>"“MyHeritage.” Wikipedia, Wikimedia Foundation, 18 Apr. 2019, [http://en.wikipedia.org/wiki/MyHeritage. en.wikipedia.org/wiki/MyHeritage]"</ref>. [https://www.geni.com Geni], another ancestry company was bought by MyHeritage in 2012<ref>"Geni Is Joining The MyHeritage Family!"https://www.geni.com/blog/geni-is-joining-the-myheritage-family-378424.html</ref>. Geni helps connect people with ancestors, or make ancestral connections through generational family trees. The platform allows users to find and connect to relatives that might belong to their heritage or family tree, work with them, while organizing their relations. Their goal is to make one large tree called the World Family Tree.<ref>Rick Crume, "Quick Guide to the Geni Family Tree Website" https://www.familytreemagazine.com/premium/geni-quick-guide/, Feb 23 2015 </ref><br />
|-<br />
| [https://www.archives.com/genealogy/dna-testing.html Archives] || A service that provides more indebt genealogical data from users who are already familiar with their genealogy.<br />
|-<br />
| [https://www.findmypast.com/?ds_kid=43700029751096390&gclid=EAIaIQobChMIvej_itHm4QIVkrrACh2i7glpEAAYASAAEgKaTfD_BwE&gclsrc=aw.ds FindMyPast] || Provides genealogical services to individuals with little to no personal genetics-related knowledge, such as individuals who have been adopted. Results are returned through easily digestible and enable further action for research <ref>Top Ten Reviews. “https://www.toptenreviews.com/services/home/best-genealogy-websites/.</ref>.<br />
|-<br />
<br />
|}<br />
<br />
=== Benefits of Ancestry Data ===<br />
Hi, I am testing this. Whether it be learning about one's genetic predispositions to medical conditions, or discovering your family history, accessing one's ancestry data offers numerous potential benefits. For instance, 23andMe offers over 100 tests that provide consumers with a variety of personalized health data. You can discover what dominant traits will be passed down to offspring, what genes do and do not affect your health, and what foods you should and shouldn’t eat <ref>23andMe. “Our Health + Ancestry DNA Service.” 23andMe, [http://www.23andme.com/dna-health-ancestry/ www.23andme.com/dna-health-ancestry/].</ref>. 23andMe has used their ancestry and DNA databases to aid in research as well. A study conducted by Dr. Abraham Palmer and his team at the University of California - San Diego School of Medicine used over 20,000 consenting 23andMe users to determine that there is a connection between impulsiveness and drug use in humans <ref>23andMe. “Genetic Study of Impulsiveness Reveals Associations with Drug Use.” 23andMe Blog, 4 Feb. 2019, [http://blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/ blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/].</ref>. Other studies have identified many genes related to depression and other mental illnesses. Though it is frowned upon<ref> “What Are the Benefits and Risks of Direct-to-Consumer Genetic Testing? - Genetics Home Reference - NIH.” U.S. National Library of Medicine, National Institutes of Health, ghr.nlm.nih.gov/primer/dtcgenetictesting/dtcrisksbenefits.</ref> by healthcare professionals to make medical decisions based off of ancestry data - without input from genetic counselors, physicians, etc. - purchasing ancestry data services is less expensive than hiring a professional genealogist. Consumers are able to exploit the power of personal genomics and gain at least some insight regarding their lineage, family history, and/or genetic predispositions. <br />
<br />
Moreover, consumers may find distant (or not-so-distant) relatives through ancestry databases. These services provide consumers with a unique opportunity to form connections with living biological family members who would have otherwise remained unknown. There are also many consumers of ancestry data who simply employ these services because they enjoy examining, learning about and/or discovering their genealogy - almost as if it were a hobby.<br />
<br />
In recent popular culture, DNA testing databases were leveraged in order to capture the [[Wikipedia:Golden_State_Killer|Golden State Killer]]. Law enforcement used DNA profiles from ancestry sites to catch and identify the killer by first locating his relatives <ref>Romano, Aja. “DNA Profiles from Ancestry Websites Helped Identify the Golden State Killer Suspect.” Vox, Vox, 27 Apr. 2018, [http://www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match].</ref>. This is becoming an increasingly common method for law enforcement and forensic science, which has lead to an innovative and more efficient (albeit controversial) way to track down criminals. Furthermore, the same technology that is being used to solve decades-old crimes is being adopted by genealogists to identify victims of crime across the country as well. Currently, Private DNA test kits like Ancestry and 23andMe are closed to law enforcement due to privacy concerns but users can upload their genetic code site to public sites like GEDMatch which law enforcement officials are able to access <ref name = "Anguiano"> Anguiano, Barbara. “Using Genetic Genealogy To Identify Unknown Crime Victims, Sometimes Decades Later.” NPR, NPR, 8 Jan. 2019, www.npr.org/2019/01/08/682925589/using-genetic-genealogy-to-identify-unknown-crime-victims-sometimes-decades-late.</ref><br />
<br />
=== Consequences of Ancestry Data ===<br />
However, as technology expands to allow for the additional applications of ancestry data, numerous consequences have emerged and been brought to public attention.<br />
Just as Facebook and other social media platforms sell and share user data with their partners, 23andMe has been known to do the same with pharmaceutical companies (e.g., a $300 million-dollar deal with GlaxoSmithKline <ref>Martin, Nicole. “How DNA Companies Like Ancestry And 23andMe Are Using Your Genetic Data.” Forbes, Forbes Magazine, 5 Dec. 2018, [http://www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189 www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189].</ref>). Direct-to-consumer companies disclose extremely personal (and private) genetic data to outsider organizations for research purposes, among other reasons. <br />
<br />
Further, once the DNA data and ancestry data has been processed, it is almost impossible for the data to be removed from the site. Unintentional sharing is also very common which often generates discomfort or distrust among customers. <ref>Brodwin, Erin. “DNA-Testing Company 23andMe Has Signed a $300 Million Deal with a Drug Giant. Here's How to Delete Your Data If That Freaks You out.” Business Insider, Business Insider, 25 July 2018, [http://www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7 www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7].</ref>. Even though all genetic information is anonymized - and (apparently) cannot be traced back to its owner - it should be protected as if it were each customer's social security number. As technological innovation continues to evolve, one can only imagine the ways in which genetic information will be introduced into everyday life. If today's consumers remain naive to the poor management of genetic information, and continue to hand over personal genetic information to companies that seek to profit off of it - said consumers are putting themselves at serious risk. There is greater potential that their personal genetic information will be leveraged against them. <br />
<br />
Norman Mooradian’s states in his paper “Importance of privacy revisited”, that people should be able to control or restrict the access of information <ref>Mooradian, Norman. “The Importance of Privacy Revisited.” Ethics and Information Technology, vol. 11, no. 3, 14 July 2009, pp. 163–174., doi:10.1007/s10676-009-9201-2.</ref>. To combat the potential consequences associated with the ways in which ancestry data is utilized today, it is important to give people the power to decide where their genetic makeup goes. Moreover, personal genomic companies are not sufficiently transparent regarding their practices and/or treatment of consumer data: individual consumers are likely not fully aware of the autonomy they are giving up when they submit their DNA to these companies. Effectively, consumers are relinquishing their right to "control or restrict" (per Mooradian) their own genetic information. AncestryDNA does offer an opt-in/opt-out feature for sharing information for research purposes when users first sign up, though it is rather difficult to find. Clearly, these companies value their ability to share or sell genetic information.<br />
<br />
Since the previously mentioned case of the Golden State Killer, a public debate regarding the release of DNA to law enforcement has emerged. Currently, websites like Ancestry.com and 23andMe have been employed by law enforcement to aid criminal investigations. While most users of these genealogy services join with simple intentions, discover simple genetic/heritage data, they are often unaware that they are additionally making their DNA accessible for reference by law enforcement. Further, the conclusions law enforcement officials are able to draw and the actions they're able to take based on partial genetic matches found remains unclear. <ref> Carolyn Crist, “Experts outline ethics issues with use of genealogy DNA to solve crimes” Reuters, 1 June. 2018, https://www.reuters.com/article/us-health-ethics-genealogy-dna/experts-outline-ethics-issues-with-use-of-genealogy-dna-to-solve-crimes-idUSKCN1IX5O6.</ref><br />
<br />
====Reliability====<br />
Ancestry data platforms are, in part, only as reliable as their customers. Incorrect data provided by a user can consequently affect a platform's data and its analysis and relationships to other users. <br />
Moreover, some platforms choose not disclose the information in their databases to their partners. The lack of transparency might indicate a lack of accuracy in the platform's data. Partners and users should remain skeptic of the results they receive. <ref>Royal, Charmaine D., et al. “Inferring Genetic Ancestry: Opportunities, Challenges, and Implications.” The American Journal of Human Genetics, vol. 86, no. 5, 2010, pp. 661–673., doi:10.1016/j.ajhg.2010.03.011. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869013/<br />
</ref>. Just with most things on the internet, the results are not 100% factual and should be taken with a grain of salt. The results are, however, a good starting point from which a customer could go on to look into their own genealogy and family tree because although the results are not always accurate, they can be realy close..<br />
<br />
== Ethics ==<br />
Ethical questions about ancestral data can arise due to the uncertainty that exists regarding how consumer data gets handled and how secure that data actually is. Without regulation, there are a number of ways in which companies can use customer's ancestral data for financial or market gain. In some cases, the incentive for large corporations to take advantage of user's data for financial gain poses some ethical dilemmas, primarily with in the case of maintaining user privacy.<br />
<br />
=== Utility of Genetic Data ===<br />
Genetic modeling companies, like 23andMe, promote their services to decipher individuals' genetic codes for healthcare related services, and to better understand individual biological functions. 23andMe offers [https://www.23andme.com/dna-health-ancestry/ Health+Ancestry] product package, which aside from providing ancestral genealogical information, provides information on "Health Predispositions," "Wellness," and "Traits." This packaged service will provide information on "how your genetics can influence your chance of developing certain health conditions," "how your genes play a role in your wellbeing and lifestyle choices," and " how your DNA influences your facial features, taste, smell, and other traits." <ref> "Find out what your DNA says about your health, traits and ancestry," 23andMe, https://www.23andme.com/dna-health-ancestry/ </ref> Genetic modeling advertising implies that it can provide personalized healthcare and behavioral analysis based on the genetic data it collects on its consumers. A [http://www.ox.ac.uk/news/2014-07-25-82-our-dna-‘functional’ 2014 Oxford University study] has found that only 8.2% of human DNA has any functionality. That implies that over 90% of human DNA has no functional role to play in human biology. Much of that DNA, according to the study, is simply genetic "baggage" that is carried over from human to human throughout generations. Genetic modeling companies that market individualized information on healthcare and traits, may be deceiving consumers by claiming to offer falsely overly-personalized products. <br />
<br />
=== Health Implications ===<br />
23andMe is one of the leading companies in online ancestry data with an aim that goes beyond processing customer's DNA for ancestry data; it also analyzes DNA to create insightful health reports for each customer's personal genome. The main DNA tests done through 23andMe provide guidance to the customer through the means of dietary suggestions or the restrictions of certain foods and valuable insight about the increased potential for disease risk within the customer's DNA. In a recent article in The Scientist<ref>Loike, John. “Opinion: Consumer DNA Testing Is Crossing into Unethical Territories.” The Scientist Magazine®, 16 Aug. 2018, [http://www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650 www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650].</ref>, Prof. John Loike claims that some of these DNA tests are not as accurate as they are perceived to be. Loike supports this claim by pointing out that 23andMe DNA test only account for 3 of the most common BCRA mutations, the mutations that are commonly used to predict breast cancer. Although 23andMe has DNA testing that addresses the 3 most common mutations, there are over 1000 BCRA mutations that a typical genealogy lab would test for. Some insurance companies are even curious to know whether or not you've taken genetics or DNA test because then they could know if later down the line you could have a genetic condition or illness, that in the end will cost the company more money.<br />
<br />
=== Paternity Tests ===<br />
[[File:madlads.jpg|center|thumbnail|A reddit post from April 19, 2019 shows a user suggesting the family of an unknowingly adopted child buy DNA test kits "and watch the world burn."]]<br />
Hello, I'm Patrick<br />
<br />
On television shows such as the American talk show ''Maury'', couples are brought on to dispute and either verify or disprove the paternity for fatherless children.<ref>The Maury Show. http://www.mauryshow.com/</ref> This may yield surprising results, such as a father with African ancestry having fair-skinned children. With DNA tests, people can be humiliated in public and sensationalize ancestry that is otherwise information people would keep more private. This brings up ethical issues of whether or not these tests should be used for public display and theatrics because of how embarrassing they can be for individuals.<br />
<br />
Reddit has a subreddit called r/23andMe that is dedicated to discussing users' test results when they come back from 23andMe DNA test kits. While this has fostered fruitful conversation and better information amongst users, the subreddit has also turned into a part-time hub for stories of DNA test kits tearing families apart. The subreddit regularly features stories of users whose tests have revealed infidelity, untold adoption, and other issues that cause rifts amongst families.<ref>Reddit r/23 and me, https://www.reddit.com/r/23andme/</ref><br />
<br />
=== White Supremacy ===<br />
One of the many claims made by rising white supremacy groups suggests that possessing pure European ancestry is the mark of superiority, and many individuals within these groups have used ancestral DNA testing as a form of validation in establishing their connection to their perceived superior ancestry. In some cases, white supremacists get results that suggest fully white European ancestry and they react with relief and celebration. Other white supremacists have taken DNA tests only to find out that they're not "pure" white, which causes them to generally discount the test results instead of re-evaluating their views on genetic hierarchies. They usually attribute non-white results to be a statistical error or affirm that family trees are the only evidence needed to prove white ancestry. Some extreme reactions include accusing Jewish people of conspiring to sabotage the DNA test results.<ref>Akpan, Nsikan. How white supremacists respond when their DNA says they're not "white." 20 Aug 2017. PBS News. https://www.pbs.org/newshour/science/white-supremacists-respond-genetics-say-theyre-not-white</ref> This is ethically challenging as these tests by nature are not always accurate, and can push forth ideas and interpretations that are false. In any case, DNA tests may create reason for hate-based groups to spread their ideologies.<br />
<br />
=== Privacy Implications ===<br />
Leading ICT ethicist Luciano Floridi argues that the right to privacy is the right to a renewable identity.<ref>Floridi, L. Ethics Inf Technol (2005) 7: 185. https://doi.org/10.1007/s10676-006-0001-7</ref> A notion that is contradicted by how contemporary ancestry data aggregators sometimes use customers' biological data without their knowledge (as discussed earlier). Recently, Danielle Teuscher had used a sperm donor to have a child and had her daughter and other members of her family take an ancestry test through 23andMe. While Danielle had not intended to find the family of her daughters donor, a woman who was not her mother was linked to her daughter as her Grandmother. Danielle decided to reach out to her donor's mother<ref>Mroz, Jacqueline. “A Mother Learns the Identity of Her Child's Grandmother. A Sperm Bank Threatens to Sue.” The New York Times, The New York Times, 16 Feb. 2019, [http://www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html].</ref>. The Sperm Bank had caught word of her reaching out, which breached their pledge to keep the donor anonymous from Danielle and her daughter, and is pursuing potential legal action. While it is in no part 23andMe's fault, ancestry data has played a large part in the ethical implications of this story as well as others.<br />
<br />
Moreover, ancestry data has shed light on aspects of peoples traits that they weren't even aware that they had. Bob Hutchinson used a DNA test kit wanting to prove his heritage, however, he discovered so much more.<ref>Kolata, Gina. “With a Simple DNA Test, Family Histories Are Rewritten.” The New York Times, The New York Times, 28 Aug. 2017, [http://www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E].</ref> Mr. Hutchinson's mother had never said much of her family other than that they were of Italian and Swedish descent, but through the tests, he learned he had African American roots. Knowing this, he worked to identify some of his relatives, whom had been told to never contact Mr. Hutchinson or his family. While it opened a new world for him, it also broke some of the ethics that the respective families followed, even if they felt they were wrong. <br />
<br />
Similarly, although it is in the interest of companies like 23andMe to keep your data private in order to maintain customer trust and protect the future of their companies, many people are concerned that utilizing these services will eliminate the privacy of your DNA. The main issue is, DNA, like [http://si410wiki.sites.uofmhosting.net/index.php/Iris_Recognition one's Iris] is unique to an individual. And with this uniqueness, comes the threat of duplication or storing the DNA to be used at a later time with the innovative technology of today. Some hold conspiracy theories that ancestry data aggregator companies collect a larger DNA sample than what is needed to perform the basic tests that consumers pay for, and that the rest of the sample is kept in a lab to be used for other experiments outside of the DNA owner's knowledge. It is commonly known that scientists are working on the duplication of DNA, and in a futuristic maybe even dystopian sense - the cloning of a human body. If one considers the numerous samples companies like 23andMe is able to collect, they could potentially be the source of samples for this research and profiting off their customers' DNA more than they are letting on.<br />
<br />
As some philosophers in information technology have suggested, informational privacy may be more effective when focused on protecting data related to users' self-identity<ref>Floridi, L., The Fourth Revolution: How the Infosphere is Reshaping Human Reality, Privacy, Oxford University Press, 2014, 101-128.</ref>. The DNA collected by companies like 23andMe can reveal vital information to individual's identity. Heritage, family, and numerous other unique traits that can be uncovered in ancestry data would likely be thought of as a part of someone's identity. If this information was then used or distributed outside of a customer's control, their privacy would have been seriously breached<ref>Shoemaker, D., Self-exposure and exposure of the self: informational privacy and the presentation of identity, 2009.</ref>.<br />
<br />
Another example of a privacy breach through a genealogical platform is the solving of the Golden State Killer case in April of 2018, where investigators were able to identify the killer by running his DNA through the genealogy platform GEDmatch and identifying his relatives. Although perfectly legal, there was question from the public as to whether this method of data collection should be allowed, since investigators were parsing through the data of people who were not suspects and who were not convicted of anything.<ref name="Guerrini">Guerrini CJ, Robinson JO, Petersen D, McGuire AL (2018) Should police have access to genetic genealogy databases? Capturing the Golden State Killer and other criminals using a controversial new forensic technique. PLoS Biol 16(10): e2006906. https://doi.org/10.1371/journal.pbio.2006906</ref><br />
<br />
===Hacking===<br />
Because of the uniqueness of DNA, hacking is a large concern. In October 2017, there was a MyHeritage breach which leaked over 92 million personal account details<ref>Brown, Kristen V. “Hack of DNA Website Exposes Data From 92 Million Accounts.” Bloomberg.com, Bloomberg, 5 June 2018, [http://www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts].</ref>. The hack became evident due to a private server, which included email addresses and hashed passwords. Because the data is private and MyHeritage understands that the trust with the consumers is extremely coveted, they keep several different servers of data. <ref>"MyHeritage breach leaks millions of account details" Makena Kelly, June 5, 2018. https://www.theverge.com/2018/6/5/17430146/dna-myheritage-ancestry-accounts-compromised-hack-breach </ref> Although MyHeritage customers passwords were leaked, their users's account contents were not, but this breach is evidence of the immense privacy concerns associated with Ancestry data.<br />
<br />
===Religious Affiliation of Ancestry Data Services===<br />
A separate potential conflict of interest that many users may not consider relates to the religious affiliations of many population genealogy databases. Namely, FamilyHeritage, founded in Salt Lake City, Utah, is sponsored by the Church of Jesus Christ Latter-day Saints, or the Mormon Church. Though FamilyHeritage offers a widely used service whose user base extends across 70 countries,<ref> "“FamilySearch.” Wikipedia, Wikimedia Foundation, 7 Apr. 2019, [http://en.wikipedia.org/wiki/FamilySearch en.wikipedia.org/wiki/FamilySearch]."</ref> the founding motive to track ancestral data stems from the belief that people can be reunited in an afterlife. Historically, the LDS Church has collected information on the deceased in order to eternally join families through a temple ceremony.<ref>"“Genealogy Is Important to Mormons Because They Believe in Eternal Families.” Www.mormonnewsroom.org, The Church of Jesus Christ of Latter-Day Saints, 23 May 2011, [http://www.mormonnewsroom.org/topic/genealogy www.mormonnewsroom.org/topic/genealogy]."</ref> Similarly, Ancestry.com, founded in Lehi, Utah by two Brigham Young University (BYU) graduates, also has clear roots in the Mormon church.<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com en.wikipedia.org/wiki/Ancestry.com]."</ref> In fact, Ancestry.com's own records emphasize the intent for users to determine the religious affiliations of their deceased ancestors, and their website offers a "Church Histories & Records" search engine to allow users to do so.<ref>"“Using Religious Records.” Ancestry.com, Ancestry.com, [http://www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf]."</ref><ref>"Church Histories &amp; Records, [http://www.ancestry.com/search/categories/dir_church/ www.ancestry.com/search/categories/dir_church/]."</ref> Though FamilyHeritage and Ancestry.com extend their services to the general public and not just those inside the Mormon Church, religion can divide individuals as often as it unites them, and thus some users may find using an ancestry data cite sponsored by the church to be problematic.<br />
<br />
===Accuracy and the Applied Implication of Self-Identity===<br />
<br />
Ancestry DNA tests are popularly believed to reveal the regions in which our genetic makeup comes from. Results are generally given by a breakdown of our genetic makeup with regional percentages an individual’s DNA derives from. This method is adequate to provide an overview summary of an individual, however, it misleads the consumer’s understanding of what the information means.<br />
<br />
The accuracy of an individual’s results is evident when taking the same individual between different companies. Although closely related, the results won’t have the same percentages and will even include new origins of one’s genetic makeup. This is from the result of discrepancies between companies’ different DNA databases<ref>Rutherford, Adam. “How Accurate Are Online DNA Tests?” Scientific American, 15 Oct. 2018, www.scientificamerican.com/article/how-accurate-are-online-dna-tests/.</ref>. Different sources of data and privately held sample collections have contributed to discrepancies amongst all ancestry test companies. They also don’t analyze the whole strand of DNA, but target locations that most likely contribute to the distinguishability between one individual to another. Humans have around 3 billion base pairs in our genetic code, however, 99.9% of these pairs are identical to everyone. It’s from the remaining 1% of our genetic code where companies can distinguish the qualities that reveal our ancestral past. These unique identifiers are referred to as [https://en.wikipedia.org/wiki/Single-nucleotide_polymorphism Single-Nucleotide Polymorphisms] or SNPs. Depending on the company used to analyze an individual’s DNA sample, different SNPs will contribute to the results while others may be ignored<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>.<br />
<br />
Error exists in the analysis of an individual’s genetic makeup by a misinterpretation by the consumer. The percentages given in the result don’t represent the proportionality of the DNA but also an inherent variance of likelihood with the results given. Consideration must be made that genetic makeup isn’t limited by the borders of countries or regions. Particular SNP arrangements can exist everywhere in the world, however, can have higher concentrations within a region of the world. The results are a probability with a margin of error, and shouldn’t be viewed as completely accurate<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>. [https://www.23andme.com/?mdc1=true 23andMe] even includes a confidence slider to illustrate results based on certainty. If this slider is moved toward more confident, the results become increasingly vague.<br />
<br />
This misinterpretation of data has influenced individuals to reconsider their familial past to the extent of impacting their self-identity. Someone unfamiliar with distant regions tied with their DNA makeup may assume roles based on stereotypes. These results can impact the identity of their consumers from probabilities and misinterpretations. When results are inaccurate, it arises a question of ethics by considering the role these companies make in influencing individuals’ self-perspective to cause an alteration in their self-identity.<br />
<br />
==Conclusion==<br />
As demonstrated, ancestry data has been a catalyst for many different ethical concerns. Whether it has been used to interpret medical data and allowing law enforcement access to our data, to circumnavigating the privacy rules of sperm banks, it had caused some unsettling feelings for many people. It is clear that in some instances the information is used to uphold the moral good, but the underlying concerns demand more discussion. One way to ensure people's privacy, proposed by Kathleen Wallace, is to use the idea of traits, such as gender, age, Social Security Number, and more as the defining qualities of that make up our anonymity. When some of these traits are hidden from public knowledge, these people are considered to be anonymous to an extent.<ref>Wallace, K.A. Ethics and Information Technology (1999) 1: 21. https://doi.org/10.1023/A:1010066509278</ref> Another way might be to have more regulations on how companies should state clearly the possible ways they will use the data besides genealogy purpose and how they should ask informed permissions before actually using the data. Senator Chuck Schumer warns that privacy concerns are not made clear enough to consumers.<ref>"US Senator Calls on FTC to Investigate DNA Ancestry Companies" Seth Augenstein, November 27, 2017. https://www.forensicmag.com/news/2017/11/us-senator-calls-ftc-investigate-dna-ancestry-companies</ref> Consumers most private information could potentially be sold to third parties, requiring an investigation by the federal trade commission.<br />
<br />
==See Also==<br />
*[[Genealogy platforms]]<br />
*[[DNA Testing]]<br />
<br />
== References ==<br />
<br />
[[Category:2019New]]<br />
[[Category:Information Ethics]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Ancestry_data&diff=91528Ancestry data2021-02-11T18:20:36Z<p>WikiSysop: /* Paternity Tests */</p>
<hr />
<div>{{Nav-Bar|Topics##}}<br><br />
<br />
[[File:Britishfamilytree.png|400px|thumb|right|The current family tree of British Royalty]]<br />
'''Ancestry data''' is collected information tracking an individual's [[Wikipedia:Lineage_(genetic)|family lineage]] to identify their [[Wikipedia:Ethnic_origin|ethnic origin]], heritage, [[Wikipedia:Place_of_birth|place of birth]], and relatives. For thousands of years, records of [[Wikipedia:ancestor|ancestry]] data have been kept to clearly define concepts of birthright and familial succession throughout both modern and ancient civilizations.<br />
<br />
Modern day technology has changed the processes of collecting and interpreting ancestry data. Companies like [https://www.23andme.com/ 23andMe] and [https://www.ancestrydna.com/kits/?s_kwcid=ancestors+dna&gclid=Cj0KCQjw1pblBRDSARIsACfUG13YNht7Foyirz_we6B_loNzZBh9I8RnQOcmxOL1a5TtUboVHYU7fgQaAikDEALw_wcB&gclsrc=aw.ds&o_xid=79108&o_lid=79108&o_sch=Paid+Search+Non+Brand AncestryDNA] offer full ethnic background maps and potential [[Wikipedia:family_tree|family tree]] links to the general public. This thriving [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer (DTC)] ancestry data industry originates from a fascination with the concept of human identity and origin. The industry's success has lead to a new form of personalized medicines and treatment plans based on genetic makeup. As of 2014, [[Wikipedia:Genealogy|genealogy]] was a 2-billion-dollar industry, and is continuing to grow.<ref> “The Genealogy Industry: $2 Billion–and Growing!” Genealogy Gems, 11 Dec. 2014, [http://lisalouisecooke.com/2014/12/11/genealogy-industry-growing lisalouisecooke.com/2014/12/11/genealogy-industry-growing]/.</ref> These new companies have made it affordable and efficient for the general public to discover more about themselves in terms of ancestry. However, as ancestry data is applied to health care solutions, a range of ethical problems, including racial supremacy, health, and privacy, emerges. <br />
<br />
== Modern Day Use and Influence of Technology ==<br />
Since the discovery of the [[Wikipedia:Nucleic_acid_double_helix|double helix]] by [[Wikipedia:Francis_Crick|Francis Crick]] and [[Wikipedia:James_Watson|James Watson]] in 1935, scientists have worked tirelessly to better understand DNA, the [[Wikipedia:human_genome|human genome]] and its countless implications. The [[Wikipedia:Human_Genome_Project|Human Genome Project]], the first full human DNA sequence in history, cost almost $3 billion.<ref>"The Human Genome Project Completion: Frequently Asked Questions" https://www.genome.gov/11006943/human-genome-project-completion-frequently-asked-questions/</ref> In contrast with today's technological advancement, an individual can get their DNA sequenced and analyzed for personal use through a variety of popular vendors for under $100. <br />
<br />
=== How Ancestry Data Works Today ===<br />
[[File:23.jpg|1000px|thumb|left|23andMe DNA testing kit, along with its instructions]]<br />
With the popularization of discovering familial lineage online, it has become incredibly simple for a person to receive their ancestry data. Interested parties simply purchase a kit from a direct-to-consumer DNA testing company, such as [[Wikipedia:23andMe|23andMe]] or any of the other 25 major competitors, follow the [[Wikipedia:DNA|DNA]] harvesting directions (i.e. spit into a small plastic tube), and send the completed test kit back to the company. Once a DNA sample is received, the company will process and sequence the DNA. In the case of 23andMe, this process takes 3-5 weeks.<ref> "When Will My Results Be Ready?" 23andMe, https://customercare.23andme.com/hc/en-us/articles/202904740-When-will-my-results-be-ready- </ref> With the help of trained professionals and [[algorithms]], DNA sequencing can provide information that changes lives. Robin Smith, head of 23andMe’s Ancestry program explains how the algorithm works: "it takes an entire genome and chunks it up...It takes little pieces, and for each piece, it compares it against the reference data set. It compares it against British; it compares it against West African; it goes through the entire list, and it spits out a probability for [where that piece of DNA came from]" <ref>Letzter, Rafi. “How Do DNA Ancestry Tests Really Work?” LiveScience, Purch, 4 June 2018, [http://www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html].</ref>. DNA sequencing also has the capability to speculate potential relatives based on near complete matches of DNA, and boasts very accurate results for those considered “Close Family” or “First Cousins”. <br />
<br />
Overall, technological advancements have made it relatively easy for the general public to access their genetic identity. With few barriers to entry, a relatively low cost, and a quick turnaround, access to ancestral data is quite easy. However, the modern applications of genetic data have become far more contentious when considering the ethical implications of a large quantity of ancestral data.<br />
<br />
===Genealogy Companies===<br />
There are many different [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer personal genomic websites] that allow individuals to receive a report of their ancestry data. Distinguishable characteristics among these genealogy databases aside, the sheer variety in platforms and the user base they each hold demonstrates the true prevalence of ancestry data in modern life. Different sites use different reference databases to compare your genetic information. Thus, results may differ between different companies from the same genetic information.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Company !! Description<br />
|-<br />
| [https://www.ancestry.com/ Ancestry] || One of the most popular direct-to-consumer personal genomic websites with 3 million paying members that offers access to 10 billion historical reports<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com. en.wikipedia.org/wiki/Ancestry.com]"</ref>.<br />
|-<br />
| [https://www.23andme.com/?mdc1=true 23 and Me] || A DNA test kit service that provides over 125 reports in an individual's ancestry, health predispositions, wellness, carrier status, and traits <ref>23andMe. “DNA Genetic Testing &amp; Analysis.” 23andMe, [https://www.23andme.com/23andMe]</ref>.<br />
|-<br />
| [https://www.familysearch.org/en/ Family Search] || Free of charge genealogy database service that offers comprehensive sequencing and analysis of personal DNA.<br />
|-<br />
| [https://www.myheritage.com/?utm_source=ppc_google&utm_medium=cpc&utm_campaign=mh_search_us_en_des_mul_exact_myheritage&utm_content=289683747520&utm_term=my+heritage&tr_camp_id=344023924&tr_ad_group=myheritage&tr_ag_id=24241928044&tr_placement=&tr_device=c&tr_account=904-055-9108&keyword=&tr_size=&recordtype=&recordlocation=&gclid=EAIaIQobChMIz-_TxKzm4QIVgbbACh1Qag_hEAAYASAAEgKONPD_BwE My Heritage] || Israel-based company that provides a DNA test kit service that is delivered to consumers' homes and processed quicker than any other service option. The company supports 92 million users worldwide<ref>"“MyHeritage.” Wikipedia, Wikimedia Foundation, 18 Apr. 2019, [http://en.wikipedia.org/wiki/MyHeritage. en.wikipedia.org/wiki/MyHeritage]"</ref>. [https://www.geni.com Geni], another ancestry company was bought by MyHeritage in 2012<ref>"Geni Is Joining The MyHeritage Family!"https://www.geni.com/blog/geni-is-joining-the-myheritage-family-378424.html</ref>. Geni helps connect people with ancestors, or make ancestral connections through generational family trees. The platform allows users to find and connect to relatives that might belong to their heritage or family tree, work with them, while organizing their relations. Their goal is to make one large tree called the World Family Tree.<ref>Rick Crume, "Quick Guide to the Geni Family Tree Website" https://www.familytreemagazine.com/premium/geni-quick-guide/, Feb 23 2015 </ref><br />
|-<br />
| [https://www.archives.com/genealogy/dna-testing.html Archives] || A service that provides more indebt genealogical data from users who are already familiar with their genealogy.<br />
|-<br />
| [https://www.findmypast.com/?ds_kid=43700029751096390&gclid=EAIaIQobChMIvej_itHm4QIVkrrACh2i7glpEAAYASAAEgKaTfD_BwE&gclsrc=aw.ds FindMyPast] || Provides genealogical services to individuals with little to no personal genetics-related knowledge, such as individuals who have been adopted. Results are returned through easily digestible and enable further action for research <ref>Top Ten Reviews. “https://www.toptenreviews.com/services/home/best-genealogy-websites/.</ref>.<br />
|-<br />
<br />
|}<br />
<br />
=== Benefits of Ancestry Data ===<br />
Hi, I am testing this. Whether it be learning about one's genetic predispositions to medical conditions, or discovering your family history, accessing one's ancestry data offers numerous potential benefits. For instance, 23andMe offers over 100 tests that provide consumers with a variety of personalized health data. You can discover what dominant traits will be passed down to offspring, what genes do and do not affect your health, and what foods you should and shouldn’t eat <ref>23andMe. “Our Health + Ancestry DNA Service.” 23andMe, [http://www.23andme.com/dna-health-ancestry/ www.23andme.com/dna-health-ancestry/].</ref>. 23andMe has used their ancestry and DNA databases to aid in research as well. A study conducted by Dr. Abraham Palmer and his team at the University of California - San Diego School of Medicine used over 20,000 consenting 23andMe users to determine that there is a connection between impulsiveness and drug use in humans <ref>23andMe. “Genetic Study of Impulsiveness Reveals Associations with Drug Use.” 23andMe Blog, 4 Feb. 2019, [http://blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/ blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/].</ref>. Other studies have identified many genes related to depression and other mental illnesses. Though it is frowned upon<ref> “What Are the Benefits and Risks of Direct-to-Consumer Genetic Testing? - Genetics Home Reference - NIH.” U.S. National Library of Medicine, National Institutes of Health, ghr.nlm.nih.gov/primer/dtcgenetictesting/dtcrisksbenefits.</ref> by healthcare professionals to make medical decisions based off of ancestry data - without input from genetic counselors, physicians, etc. - purchasing ancestry data services is less expensive than hiring a professional genealogist. Consumers are able to exploit the power of personal genomics and gain at least some insight regarding their lineage, family history, and/or genetic predispositions. <br />
<br />
Moreover, consumers may find distant (or not-so-distant) relatives through ancestry databases. These services provide consumers with a unique opportunity to form connections with living biological family members who would have otherwise remained unknown. There are also many consumers of ancestry data who simply employ these services because they enjoy examining, learning about and/or discovering their genealogy - almost as if it were a hobby.<br />
<br />
In recent popular culture, DNA testing databases were leveraged in order to capture the [[Wikipedia:Golden_State_Killer|Golden State Killer]]. Law enforcement used DNA profiles from ancestry sites to catch and identify the killer by first locating his relatives <ref>Romano, Aja. “DNA Profiles from Ancestry Websites Helped Identify the Golden State Killer Suspect.” Vox, Vox, 27 Apr. 2018, [http://www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match].</ref>. This is becoming an increasingly common method for law enforcement and forensic science, which has lead to an innovative and more efficient (albeit controversial) way to track down criminals. Furthermore, the same technology that is being used to solve decades-old crimes is being adopted by genealogists to identify victims of crime across the country as well. Currently, Private DNA test kits like Ancestry and 23andMe are closed to law enforcement due to privacy concerns but users can upload their genetic code site to public sites like GEDMatch which law enforcement officials are able to access <ref name = "Anguiano"> Anguiano, Barbara. “Using Genetic Genealogy To Identify Unknown Crime Victims, Sometimes Decades Later.” NPR, NPR, 8 Jan. 2019, www.npr.org/2019/01/08/682925589/using-genetic-genealogy-to-identify-unknown-crime-victims-sometimes-decades-late.</ref><br />
<br />
=== Consequences of Ancestry Data ===<br />
However, as technology expands to allow for the additional applications of ancestry data, numerous consequences have emerged and been brought to public attention.<br />
Just as Facebook and other social media platforms sell and share user data with their partners, 23andMe has been known to do the same with pharmaceutical companies (e.g., a $300 million-dollar deal with GlaxoSmithKline <ref>Martin, Nicole. “How DNA Companies Like Ancestry And 23andMe Are Using Your Genetic Data.” Forbes, Forbes Magazine, 5 Dec. 2018, [http://www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189 www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189].</ref>). Direct-to-consumer companies disclose extremely personal (and private) genetic data to outsider organizations for research purposes, among other reasons. <br />
<br />
Further, once the DNA data and ancestry data has been processed, it is almost impossible for the data to be removed from the site. Unintentional sharing is also very common which often generates discomfort or distrust among customers. <ref>Brodwin, Erin. “DNA-Testing Company 23andMe Has Signed a $300 Million Deal with a Drug Giant. Here's How to Delete Your Data If That Freaks You out.” Business Insider, Business Insider, 25 July 2018, [http://www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7 www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7].</ref>. Even though all genetic information is anonymized - and (apparently) cannot be traced back to its owner - it should be protected as if it were each customer's social security number. As technological innovation continues to evolve, one can only imagine the ways in which genetic information will be introduced into everyday life. If today's consumers remain naive to the poor management of genetic information, and continue to hand over personal genetic information to companies that seek to profit off of it - said consumers are putting themselves at serious risk. There is greater potential that their personal genetic information will be leveraged against them. <br />
<br />
Norman Mooradian’s states in his paper “Importance of privacy revisited”, that people should be able to control or restrict the access of information <ref>Mooradian, Norman. “The Importance of Privacy Revisited.” Ethics and Information Technology, vol. 11, no. 3, 14 July 2009, pp. 163–174., doi:10.1007/s10676-009-9201-2.</ref>. To combat the potential consequences associated with the ways in which ancestry data is utilized today, it is important to give people the power to decide where their genetic makeup goes. Moreover, personal genomic companies are not sufficiently transparent regarding their practices and/or treatment of consumer data: individual consumers are likely not fully aware of the autonomy they are giving up when they submit their DNA to these companies. Effectively, consumers are relinquishing their right to "control or restrict" (per Mooradian) their own genetic information. AncestryDNA does offer an opt-in/opt-out feature for sharing information for research purposes when users first sign up, though it is rather difficult to find. Clearly, these companies value their ability to share or sell genetic information.<br />
<br />
Since the previously mentioned case of the Golden State Killer, a public debate regarding the release of DNA to law enforcement has emerged. Currently, websites like Ancestry.com and 23andMe have been employed by law enforcement to aid criminal investigations. While most users of these genealogy services join with simple intentions, discover simple genetic/heritage data, they are often unaware that they are additionally making their DNA accessible for reference by law enforcement. Further, the conclusions law enforcement officials are able to draw and the actions they're able to take based on partial genetic matches found remains unclear. <ref> Carolyn Crist, “Experts outline ethics issues with use of genealogy DNA to solve crimes” Reuters, 1 June. 2018, https://www.reuters.com/article/us-health-ethics-genealogy-dna/experts-outline-ethics-issues-with-use-of-genealogy-dna-to-solve-crimes-idUSKCN1IX5O6.</ref><br />
<br />
====Reliability====<br />
Ancestry data platforms are, in part, only as reliable as their customers. Incorrect data provided by a user can consequently affect a platform's data and its analysis and relationships to other users. <br />
Moreover, some platforms choose not disclose the information in their databases to their partners. The lack of transparency might indicate a lack of accuracy in the platform's data. Partners and users should remain skeptic of the results they receive. <ref>Royal, Charmaine D., et al. “Inferring Genetic Ancestry: Opportunities, Challenges, and Implications.” The American Journal of Human Genetics, vol. 86, no. 5, 2010, pp. 661–673., doi:10.1016/j.ajhg.2010.03.011. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869013/<br />
</ref>. Just with most things on the internet, the results are not 100% factual and should be taken with a grain of salt. The results are, however, a good starting point from which a customer could go on to look into their own genealogy and family tree because although the results are not always accurate, they can be realy close..<br />
<br />
== Ethics ==<br />
Ethical questions about ancestral data can arise due to the uncertainty that exists regarding how consumer data gets handled and how secure that data actually is. Without regulation, there are a number of ways in which companies can use customer's ancestral data for financial or market gain. In some cases, the incentive for large corporations to take advantage of user's data for financial gain poses some ethical dilemmas, primarily with in the case of maintaining user privacy.<br />
<br />
=== Utility of Genetic Data ===<br />
Genetic modeling companies, like 23andMe, promote their services to decipher individuals' genetic codes for healthcare related services, and to better understand individual biological functions. 23andMe offers [https://www.23andme.com/dna-health-ancestry/ Health+Ancestry] product package, which aside from providing ancestral genealogical information, provides information on "Health Predispositions," "Wellness," and "Traits." This packaged service will provide information on "how your genetics can influence your chance of developing certain health conditions," "how your genes play a role in your wellbeing and lifestyle choices," and " how your DNA influences your facial features, taste, smell, and other traits." <ref> "Find out what your DNA says about your health, traits and ancestry," 23andMe, https://www.23andme.com/dna-health-ancestry/ </ref> Genetic modeling advertising implies that it can provide personalized healthcare and behavioral analysis based on the genetic data it collects on its consumers. A [http://www.ox.ac.uk/news/2014-07-25-82-our-dna-‘functional’ 2014 Oxford University study] has found that only 8.2% of human DNA has any functionality. That implies that over 90% of human DNA has no functional role to play in human biology. Much of that DNA, according to the study, is simply genetic "baggage" that is carried over from human to human throughout generations. Genetic modeling companies that market individualized information on healthcare and traits, may be deceiving consumers by claiming to offer falsely overly-personalized products. <br />
<br />
=== Health Implications ===<br />
23andMe is one of the leading companies in online ancestry data with an aim that goes beyond processing customer's DNA for ancestry data; it also analyzes DNA to create insightful health reports for each customer's personal genome. The main DNA tests done through 23andMe provide guidance to the customer through the means of dietary suggestions or the restrictions of certain foods and valuable insight about the increased potential for disease risk within the customer's DNA. In a recent article in The Scientist<ref>Loike, John. “Opinion: Consumer DNA Testing Is Crossing into Unethical Territories.” The Scientist Magazine®, 16 Aug. 2018, [http://www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650 www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650].</ref>, Prof. John Loike claims that some of these DNA tests are not as accurate as they are perceived to be. Loike supports this claim by pointing out that 23andMe DNA test only account for 3 of the most common BCRA mutations, the mutations that are commonly used to predict breast cancer. Although 23andMe has DNA testing that addresses the 3 most common mutations, there are over 1000 BCRA mutations that a typical genealogy lab would test for. Some insurance companies are even curious to know whether or not you've taken genetics or DNA test because then they could know if later down the line you could have a genetic condition or illness, that in the end will cost the company more money.<br />
<br />
=== Paternity Tests ===<br />
<br />
Hello, I'm Patrick<br />
<br />
On television shows such as the American talk show ''Maury'', couples are brought on to dispute and either verify or disprove the paternity for fatherless children.<ref>The Maury Show. http://www.mauryshow.com/</ref> This may yield surprising results, such as a father with African ancestry having fair-skinned children. With DNA tests, people can be humiliated in public and sensationalize ancestry that is otherwise information people would keep more private. This brings up ethical issues of whether or not these tests should be used for public display and theatrics because of how embarrassing they can be for individuals.<br />
<br />
Reddit has a subreddit called r/23andMe that is dedicated to discussing users' test results when they come back from 23andMe DNA test kits. While this has fostered fruitful conversation and better information amongst users, the subreddit has also turned into a part-time hub for stories of DNA test kits tearing families apart. The subreddit regularly features stories of users whose tests have revealed infidelity, untold adoption, and other issues that cause rifts amongst families.<ref>Reddit r/23 and me, https://www.reddit.com/r/23andme/</ref><br />
<br />
=== White Supremacy ===<br />
One of the many claims made by rising white supremacy groups suggests that possessing pure European ancestry is the mark of superiority, and many individuals within these groups have used ancestral DNA testing as a form of validation in establishing their connection to their perceived superior ancestry. In some cases, white supremacists get results that suggest fully white European ancestry and they react with relief and celebration. Other white supremacists have taken DNA tests only to find out that they're not "pure" white, which causes them to generally discount the test results instead of re-evaluating their views on genetic hierarchies. They usually attribute non-white results to be a statistical error or affirm that family trees are the only evidence needed to prove white ancestry. Some extreme reactions include accusing Jewish people of conspiring to sabotage the DNA test results.<ref>Akpan, Nsikan. How white supremacists respond when their DNA says they're not "white." 20 Aug 2017. PBS News. https://www.pbs.org/newshour/science/white-supremacists-respond-genetics-say-theyre-not-white</ref> This is ethically challenging as these tests by nature are not always accurate, and can push forth ideas and interpretations that are false. In any case, DNA tests may create reason for hate-based groups to spread their ideologies.<br />
<br />
=== Privacy Implications ===<br />
Leading ICT ethicist Luciano Floridi argues that the right to privacy is the right to a renewable identity.<ref>Floridi, L. Ethics Inf Technol (2005) 7: 185. https://doi.org/10.1007/s10676-006-0001-7</ref> A notion that is contradicted by how contemporary ancestry data aggregators sometimes use customers' biological data without their knowledge (as discussed earlier). Recently, Danielle Teuscher had used a sperm donor to have a child and had her daughter and other members of her family take an ancestry test through 23andMe. While Danielle had not intended to find the family of her daughters donor, a woman who was not her mother was linked to her daughter as her Grandmother. Danielle decided to reach out to her donor's mother<ref>Mroz, Jacqueline. “A Mother Learns the Identity of Her Child's Grandmother. A Sperm Bank Threatens to Sue.” The New York Times, The New York Times, 16 Feb. 2019, [http://www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html].</ref>. The Sperm Bank had caught word of her reaching out, which breached their pledge to keep the donor anonymous from Danielle and her daughter, and is pursuing potential legal action. While it is in no part 23andMe's fault, ancestry data has played a large part in the ethical implications of this story as well as others.<br />
<br />
Moreover, ancestry data has shed light on aspects of peoples traits that they weren't even aware that they had. Bob Hutchinson used a DNA test kit wanting to prove his heritage, however, he discovered so much more.<ref>Kolata, Gina. “With a Simple DNA Test, Family Histories Are Rewritten.” The New York Times, The New York Times, 28 Aug. 2017, [http://www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E].</ref> Mr. Hutchinson's mother had never said much of her family other than that they were of Italian and Swedish descent, but through the tests, he learned he had African American roots. Knowing this, he worked to identify some of his relatives, whom had been told to never contact Mr. Hutchinson or his family. While it opened a new world for him, it also broke some of the ethics that the respective families followed, even if they felt they were wrong. <br />
<br />
Similarly, although it is in the interest of companies like 23andMe to keep your data private in order to maintain customer trust and protect the future of their companies, many people are concerned that utilizing these services will eliminate the privacy of your DNA. The main issue is, DNA, like [http://si410wiki.sites.uofmhosting.net/index.php/Iris_Recognition one's Iris] is unique to an individual. And with this uniqueness, comes the threat of duplication or storing the DNA to be used at a later time with the innovative technology of today. Some hold conspiracy theories that ancestry data aggregator companies collect a larger DNA sample than what is needed to perform the basic tests that consumers pay for, and that the rest of the sample is kept in a lab to be used for other experiments outside of the DNA owner's knowledge. It is commonly known that scientists are working on the duplication of DNA, and in a futuristic maybe even dystopian sense - the cloning of a human body. If one considers the numerous samples companies like 23andMe is able to collect, they could potentially be the source of samples for this research and profiting off their customers' DNA more than they are letting on.<br />
<br />
As some philosophers in information technology have suggested, informational privacy may be more effective when focused on protecting data related to users' self-identity<ref>Floridi, L., The Fourth Revolution: How the Infosphere is Reshaping Human Reality, Privacy, Oxford University Press, 2014, 101-128.</ref>. The DNA collected by companies like 23andMe can reveal vital information to individual's identity. Heritage, family, and numerous other unique traits that can be uncovered in ancestry data would likely be thought of as a part of someone's identity. If this information was then used or distributed outside of a customer's control, their privacy would have been seriously breached<ref>Shoemaker, D., Self-exposure and exposure of the self: informational privacy and the presentation of identity, 2009.</ref>.<br />
<br />
Another example of a privacy breach through a genealogical platform is the solving of the Golden State Killer case in April of 2018, where investigators were able to identify the killer by running his DNA through the genealogy platform GEDmatch and identifying his relatives. Although perfectly legal, there was question from the public as to whether this method of data collection should be allowed, since investigators were parsing through the data of people who were not suspects and who were not convicted of anything.<ref name="Guerrini">Guerrini CJ, Robinson JO, Petersen D, McGuire AL (2018) Should police have access to genetic genealogy databases? Capturing the Golden State Killer and other criminals using a controversial new forensic technique. PLoS Biol 16(10): e2006906. https://doi.org/10.1371/journal.pbio.2006906</ref><br />
<br />
===Hacking===<br />
Because of the uniqueness of DNA, hacking is a large concern. In October 2017, there was a MyHeritage breach which leaked over 92 million personal account details<ref>Brown, Kristen V. “Hack of DNA Website Exposes Data From 92 Million Accounts.” Bloomberg.com, Bloomberg, 5 June 2018, [http://www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts].</ref>. The hack became evident due to a private server, which included email addresses and hashed passwords. Because the data is private and MyHeritage understands that the trust with the consumers is extremely coveted, they keep several different servers of data. <ref>"MyHeritage breach leaks millions of account details" Makena Kelly, June 5, 2018. https://www.theverge.com/2018/6/5/17430146/dna-myheritage-ancestry-accounts-compromised-hack-breach </ref> Although MyHeritage customers passwords were leaked, their users's account contents were not, but this breach is evidence of the immense privacy concerns associated with Ancestry data.<br />
<br />
===Religious Affiliation of Ancestry Data Services===<br />
A separate potential conflict of interest that many users may not consider relates to the religious affiliations of many population genealogy databases. Namely, FamilyHeritage, founded in Salt Lake City, Utah, is sponsored by the Church of Jesus Christ Latter-day Saints, or the Mormon Church. Though FamilyHeritage offers a widely used service whose user base extends across 70 countries,<ref> "“FamilySearch.” Wikipedia, Wikimedia Foundation, 7 Apr. 2019, [http://en.wikipedia.org/wiki/FamilySearch en.wikipedia.org/wiki/FamilySearch]."</ref> the founding motive to track ancestral data stems from the belief that people can be reunited in an afterlife. Historically, the LDS Church has collected information on the deceased in order to eternally join families through a temple ceremony.<ref>"“Genealogy Is Important to Mormons Because They Believe in Eternal Families.” Www.mormonnewsroom.org, The Church of Jesus Christ of Latter-Day Saints, 23 May 2011, [http://www.mormonnewsroom.org/topic/genealogy www.mormonnewsroom.org/topic/genealogy]."</ref> Similarly, Ancestry.com, founded in Lehi, Utah by two Brigham Young University (BYU) graduates, also has clear roots in the Mormon church.<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com en.wikipedia.org/wiki/Ancestry.com]."</ref> In fact, Ancestry.com's own records emphasize the intent for users to determine the religious affiliations of their deceased ancestors, and their website offers a "Church Histories & Records" search engine to allow users to do so.<ref>"“Using Religious Records.” Ancestry.com, Ancestry.com, [http://www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf]."</ref><ref>"Church Histories &amp; Records, [http://www.ancestry.com/search/categories/dir_church/ www.ancestry.com/search/categories/dir_church/]."</ref> Though FamilyHeritage and Ancestry.com extend their services to the general public and not just those inside the Mormon Church, religion can divide individuals as often as it unites them, and thus some users may find using an ancestry data cite sponsored by the church to be problematic.<br />
<br />
===Accuracy and the Applied Implication of Self-Identity===<br />
<br />
Ancestry DNA tests are popularly believed to reveal the regions in which our genetic makeup comes from. Results are generally given by a breakdown of our genetic makeup with regional percentages an individual’s DNA derives from. This method is adequate to provide an overview summary of an individual, however, it misleads the consumer’s understanding of what the information means.<br />
<br />
The accuracy of an individual’s results is evident when taking the same individual between different companies. Although closely related, the results won’t have the same percentages and will even include new origins of one’s genetic makeup. This is from the result of discrepancies between companies’ different DNA databases<ref>Rutherford, Adam. “How Accurate Are Online DNA Tests?” Scientific American, 15 Oct. 2018, www.scientificamerican.com/article/how-accurate-are-online-dna-tests/.</ref>. Different sources of data and privately held sample collections have contributed to discrepancies amongst all ancestry test companies. They also don’t analyze the whole strand of DNA, but target locations that most likely contribute to the distinguishability between one individual to another. Humans have around 3 billion base pairs in our genetic code, however, 99.9% of these pairs are identical to everyone. It’s from the remaining 1% of our genetic code where companies can distinguish the qualities that reveal our ancestral past. These unique identifiers are referred to as [https://en.wikipedia.org/wiki/Single-nucleotide_polymorphism Single-Nucleotide Polymorphisms] or SNPs. Depending on the company used to analyze an individual’s DNA sample, different SNPs will contribute to the results while others may be ignored<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>.<br />
<br />
Error exists in the analysis of an individual’s genetic makeup by a misinterpretation by the consumer. The percentages given in the result don’t represent the proportionality of the DNA but also an inherent variance of likelihood with the results given. Consideration must be made that genetic makeup isn’t limited by the borders of countries or regions. Particular SNP arrangements can exist everywhere in the world, however, can have higher concentrations within a region of the world. The results are a probability with a margin of error, and shouldn’t be viewed as completely accurate<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>. [https://www.23andme.com/?mdc1=true 23andMe] even includes a confidence slider to illustrate results based on certainty. If this slider is moved toward more confident, the results become increasingly vague.<br />
<br />
This misinterpretation of data has influenced individuals to reconsider their familial past to the extent of impacting their self-identity. Someone unfamiliar with distant regions tied with their DNA makeup may assume roles based on stereotypes. These results can impact the identity of their consumers from probabilities and misinterpretations. When results are inaccurate, it arises a question of ethics by considering the role these companies make in influencing individuals’ self-perspective to cause an alteration in their self-identity.<br />
<br />
==Conclusion==<br />
As demonstrated, ancestry data has been a catalyst for many different ethical concerns. Whether it has been used to interpret medical data and allowing law enforcement access to our data, to circumnavigating the privacy rules of sperm banks, it had caused some unsettling feelings for many people. It is clear that in some instances the information is used to uphold the moral good, but the underlying concerns demand more discussion. One way to ensure people's privacy, proposed by Kathleen Wallace, is to use the idea of traits, such as gender, age, Social Security Number, and more as the defining qualities of that make up our anonymity. When some of these traits are hidden from public knowledge, these people are considered to be anonymous to an extent.<ref>Wallace, K.A. Ethics and Information Technology (1999) 1: 21. https://doi.org/10.1023/A:1010066509278</ref> Another way might be to have more regulations on how companies should state clearly the possible ways they will use the data besides genealogy purpose and how they should ask informed permissions before actually using the data. Senator Chuck Schumer warns that privacy concerns are not made clear enough to consumers.<ref>"US Senator Calls on FTC to Investigate DNA Ancestry Companies" Seth Augenstein, November 27, 2017. https://www.forensicmag.com/news/2017/11/us-senator-calls-ftc-investigate-dna-ancestry-companies</ref> Consumers most private information could potentially be sold to third parties, requiring an investigation by the federal trade commission.<br />
<br />
==See Also==<br />
*[[Genealogy platforms]]<br />
*[[DNA Testing]]<br />
<br />
== References ==<br />
<br />
[[Category:2019New]]<br />
[[Category:Information Ethics]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Ancestry_data&diff=91522Ancestry data2021-02-11T18:19:44Z<p>WikiSysop: /* Paternity Tests */</p>
<hr />
<div>{{Nav-Bar|Topics##}}<br><br />
<br />
[[File:Britishfamilytree.png|400px|thumb|right|The current family tree of British Royalty]]<br />
'''Ancestry data''' is collected information tracking an individual's [[Wikipedia:Lineage_(genetic)|family lineage]] to identify their [[Wikipedia:Ethnic_origin|ethnic origin]], heritage, [[Wikipedia:Place_of_birth|place of birth]], and relatives. For thousands of years, records of [[Wikipedia:ancestor|ancestry]] data have been kept to clearly define concepts of birthright and familial succession throughout both modern and ancient civilizations.<br />
<br />
Modern day technology has changed the processes of collecting and interpreting ancestry data. Companies like [https://www.23andme.com/ 23andMe] and [https://www.ancestrydna.com/kits/?s_kwcid=ancestors+dna&gclid=Cj0KCQjw1pblBRDSARIsACfUG13YNht7Foyirz_we6B_loNzZBh9I8RnQOcmxOL1a5TtUboVHYU7fgQaAikDEALw_wcB&gclsrc=aw.ds&o_xid=79108&o_lid=79108&o_sch=Paid+Search+Non+Brand AncestryDNA] offer full ethnic background maps and potential [[Wikipedia:family_tree|family tree]] links to the general public. This thriving [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer (DTC)] ancestry data industry originates from a fascination with the concept of human identity and origin. The industry's success has lead to a new form of personalized medicines and treatment plans based on genetic makeup. As of 2014, [[Wikipedia:Genealogy|genealogy]] was a 2-billion-dollar industry, and is continuing to grow.<ref> “The Genealogy Industry: $2 Billion–and Growing!” Genealogy Gems, 11 Dec. 2014, [http://lisalouisecooke.com/2014/12/11/genealogy-industry-growing lisalouisecooke.com/2014/12/11/genealogy-industry-growing]/.</ref> These new companies have made it affordable and efficient for the general public to discover more about themselves in terms of ancestry. However, as ancestry data is applied to health care solutions, a range of ethical problems, including racial supremacy, health, and privacy, emerges. <br />
<br />
== Modern Day Use and Influence of Technology ==<br />
Since the discovery of the [[Wikipedia:Nucleic_acid_double_helix|double helix]] by [[Wikipedia:Francis_Crick|Francis Crick]] and [[Wikipedia:James_Watson|James Watson]] in 1935, scientists have worked tirelessly to better understand DNA, the [[Wikipedia:human_genome|human genome]] and its countless implications. The [[Wikipedia:Human_Genome_Project|Human Genome Project]], the first full human DNA sequence in history, cost almost $3 billion.<ref>"The Human Genome Project Completion: Frequently Asked Questions" https://www.genome.gov/11006943/human-genome-project-completion-frequently-asked-questions/</ref> In contrast with today's technological advancement, an individual can get their DNA sequenced and analyzed for personal use through a variety of popular vendors for under $100. <br />
<br />
=== How Ancestry Data Works Today ===<br />
[[File:23.jpg|1000px|thumb|left|23andMe DNA testing kit, along with its instructions]]<br />
With the popularization of discovering familial lineage online, it has become incredibly simple for a person to receive their ancestry data. Interested parties simply purchase a kit from a direct-to-consumer DNA testing company, such as [[Wikipedia:23andMe|23andMe]] or any of the other 25 major competitors, follow the [[Wikipedia:DNA|DNA]] harvesting directions (i.e. spit into a small plastic tube), and send the completed test kit back to the company. Once a DNA sample is received, the company will process and sequence the DNA. In the case of 23andMe, this process takes 3-5 weeks.<ref> "When Will My Results Be Ready?" 23andMe, https://customercare.23andme.com/hc/en-us/articles/202904740-When-will-my-results-be-ready- </ref> With the help of trained professionals and [[algorithms]], DNA sequencing can provide information that changes lives. Robin Smith, head of 23andMe’s Ancestry program explains how the algorithm works: "it takes an entire genome and chunks it up...It takes little pieces, and for each piece, it compares it against the reference data set. It compares it against British; it compares it against West African; it goes through the entire list, and it spits out a probability for [where that piece of DNA came from]" <ref>Letzter, Rafi. “How Do DNA Ancestry Tests Really Work?” LiveScience, Purch, 4 June 2018, [http://www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html].</ref>. DNA sequencing also has the capability to speculate potential relatives based on near complete matches of DNA, and boasts very accurate results for those considered “Close Family” or “First Cousins”. <br />
<br />
Overall, technological advancements have made it relatively easy for the general public to access their genetic identity. With few barriers to entry, a relatively low cost, and a quick turnaround, access to ancestral data is quite easy. However, the modern applications of genetic data have become far more contentious when considering the ethical implications of a large quantity of ancestral data.<br />
<br />
===Genealogy Companies===<br />
There are many different [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer personal genomic websites] that allow individuals to receive a report of their ancestry data. Distinguishable characteristics among these genealogy databases aside, the sheer variety in platforms and the user base they each hold demonstrates the true prevalence of ancestry data in modern life. Different sites use different reference databases to compare your genetic information. Thus, results may differ between different companies from the same genetic information.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Company !! Description<br />
|-<br />
| [https://www.ancestry.com/ Ancestry] || One of the most popular direct-to-consumer personal genomic websites with 3 million paying members that offers access to 10 billion historical reports<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com. en.wikipedia.org/wiki/Ancestry.com]"</ref>.<br />
|-<br />
| [https://www.23andme.com/?mdc1=true 23 and Me] || A DNA test kit service that provides over 125 reports in an individual's ancestry, health predispositions, wellness, carrier status, and traits <ref>23andMe. “DNA Genetic Testing &amp; Analysis.” 23andMe, [https://www.23andme.com/23andMe]</ref>.<br />
|-<br />
| [https://www.familysearch.org/en/ Family Search] || Free of charge genealogy database service that offers comprehensive sequencing and analysis of personal DNA.<br />
|-<br />
| [https://www.myheritage.com/?utm_source=ppc_google&utm_medium=cpc&utm_campaign=mh_search_us_en_des_mul_exact_myheritage&utm_content=289683747520&utm_term=my+heritage&tr_camp_id=344023924&tr_ad_group=myheritage&tr_ag_id=24241928044&tr_placement=&tr_device=c&tr_account=904-055-9108&keyword=&tr_size=&recordtype=&recordlocation=&gclid=EAIaIQobChMIz-_TxKzm4QIVgbbACh1Qag_hEAAYASAAEgKONPD_BwE My Heritage] || Israel-based company that provides a DNA test kit service that is delivered to consumers' homes and processed quicker than any other service option. The company supports 92 million users worldwide<ref>"“MyHeritage.” Wikipedia, Wikimedia Foundation, 18 Apr. 2019, [http://en.wikipedia.org/wiki/MyHeritage. en.wikipedia.org/wiki/MyHeritage]"</ref>. [https://www.geni.com Geni], another ancestry company was bought by MyHeritage in 2012<ref>"Geni Is Joining The MyHeritage Family!"https://www.geni.com/blog/geni-is-joining-the-myheritage-family-378424.html</ref>. Geni helps connect people with ancestors, or make ancestral connections through generational family trees. The platform allows users to find and connect to relatives that might belong to their heritage or family tree, work with them, while organizing their relations. Their goal is to make one large tree called the World Family Tree.<ref>Rick Crume, "Quick Guide to the Geni Family Tree Website" https://www.familytreemagazine.com/premium/geni-quick-guide/, Feb 23 2015 </ref><br />
|-<br />
| [https://www.archives.com/genealogy/dna-testing.html Archives] || A service that provides more indebt genealogical data from users who are already familiar with their genealogy.<br />
|-<br />
| [https://www.findmypast.com/?ds_kid=43700029751096390&gclid=EAIaIQobChMIvej_itHm4QIVkrrACh2i7glpEAAYASAAEgKaTfD_BwE&gclsrc=aw.ds FindMyPast] || Provides genealogical services to individuals with little to no personal genetics-related knowledge, such as individuals who have been adopted. Results are returned through easily digestible and enable further action for research <ref>Top Ten Reviews. “https://www.toptenreviews.com/services/home/best-genealogy-websites/.</ref>.<br />
|-<br />
<br />
|}<br />
<br />
=== Benefits of Ancestry Data ===<br />
Hi, I am testing this. Whether it be learning about one's genetic predispositions to medical conditions, or discovering your family history, accessing one's ancestry data offers numerous potential benefits. For instance, 23andMe offers over 100 tests that provide consumers with a variety of personalized health data. You can discover what dominant traits will be passed down to offspring, what genes do and do not affect your health, and what foods you should and shouldn’t eat <ref>23andMe. “Our Health + Ancestry DNA Service.” 23andMe, [http://www.23andme.com/dna-health-ancestry/ www.23andme.com/dna-health-ancestry/].</ref>. 23andMe has used their ancestry and DNA databases to aid in research as well. A study conducted by Dr. Abraham Palmer and his team at the University of California - San Diego School of Medicine used over 20,000 consenting 23andMe users to determine that there is a connection between impulsiveness and drug use in humans <ref>23andMe. “Genetic Study of Impulsiveness Reveals Associations with Drug Use.” 23andMe Blog, 4 Feb. 2019, [http://blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/ blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/].</ref>. Other studies have identified many genes related to depression and other mental illnesses. Though it is frowned upon<ref> “What Are the Benefits and Risks of Direct-to-Consumer Genetic Testing? - Genetics Home Reference - NIH.” U.S. National Library of Medicine, National Institutes of Health, ghr.nlm.nih.gov/primer/dtcgenetictesting/dtcrisksbenefits.</ref> by healthcare professionals to make medical decisions based off of ancestry data - without input from genetic counselors, physicians, etc. - purchasing ancestry data services is less expensive than hiring a professional genealogist. Consumers are able to exploit the power of personal genomics and gain at least some insight regarding their lineage, family history, and/or genetic predispositions. <br />
<br />
Moreover, consumers may find distant (or not-so-distant) relatives through ancestry databases. These services provide consumers with a unique opportunity to form connections with living biological family members who would have otherwise remained unknown. There are also many consumers of ancestry data who simply employ these services because they enjoy examining, learning about and/or discovering their genealogy - almost as if it were a hobby.<br />
<br />
In recent popular culture, DNA testing databases were leveraged in order to capture the [[Wikipedia:Golden_State_Killer|Golden State Killer]]. Law enforcement used DNA profiles from ancestry sites to catch and identify the killer by first locating his relatives <ref>Romano, Aja. “DNA Profiles from Ancestry Websites Helped Identify the Golden State Killer Suspect.” Vox, Vox, 27 Apr. 2018, [http://www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match].</ref>. This is becoming an increasingly common method for law enforcement and forensic science, which has lead to an innovative and more efficient (albeit controversial) way to track down criminals. Furthermore, the same technology that is being used to solve decades-old crimes is being adopted by genealogists to identify victims of crime across the country as well. Currently, Private DNA test kits like Ancestry and 23andMe are closed to law enforcement due to privacy concerns but users can upload their genetic code site to public sites like GEDMatch which law enforcement officials are able to access <ref name = "Anguiano"> Anguiano, Barbara. “Using Genetic Genealogy To Identify Unknown Crime Victims, Sometimes Decades Later.” NPR, NPR, 8 Jan. 2019, www.npr.org/2019/01/08/682925589/using-genetic-genealogy-to-identify-unknown-crime-victims-sometimes-decades-late.</ref><br />
<br />
=== Consequences of Ancestry Data ===<br />
However, as technology expands to allow for the additional applications of ancestry data, numerous consequences have emerged and been brought to public attention.<br />
Just as Facebook and other social media platforms sell and share user data with their partners, 23andMe has been known to do the same with pharmaceutical companies (e.g., a $300 million-dollar deal with GlaxoSmithKline <ref>Martin, Nicole. “How DNA Companies Like Ancestry And 23andMe Are Using Your Genetic Data.” Forbes, Forbes Magazine, 5 Dec. 2018, [http://www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189 www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189].</ref>). Direct-to-consumer companies disclose extremely personal (and private) genetic data to outsider organizations for research purposes, among other reasons. <br />
<br />
Further, once the DNA data and ancestry data has been processed, it is almost impossible for the data to be removed from the site. Unintentional sharing is also very common which often generates discomfort or distrust among customers. <ref>Brodwin, Erin. “DNA-Testing Company 23andMe Has Signed a $300 Million Deal with a Drug Giant. Here's How to Delete Your Data If That Freaks You out.” Business Insider, Business Insider, 25 July 2018, [http://www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7 www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7].</ref>. Even though all genetic information is anonymized - and (apparently) cannot be traced back to its owner - it should be protected as if it were each customer's social security number. As technological innovation continues to evolve, one can only imagine the ways in which genetic information will be introduced into everyday life. If today's consumers remain naive to the poor management of genetic information, and continue to hand over personal genetic information to companies that seek to profit off of it - said consumers are putting themselves at serious risk. There is greater potential that their personal genetic information will be leveraged against them. <br />
<br />
Norman Mooradian’s states in his paper “Importance of privacy revisited”, that people should be able to control or restrict the access of information <ref>Mooradian, Norman. “The Importance of Privacy Revisited.” Ethics and Information Technology, vol. 11, no. 3, 14 July 2009, pp. 163–174., doi:10.1007/s10676-009-9201-2.</ref>. To combat the potential consequences associated with the ways in which ancestry data is utilized today, it is important to give people the power to decide where their genetic makeup goes. Moreover, personal genomic companies are not sufficiently transparent regarding their practices and/or treatment of consumer data: individual consumers are likely not fully aware of the autonomy they are giving up when they submit their DNA to these companies. Effectively, consumers are relinquishing their right to "control or restrict" (per Mooradian) their own genetic information. AncestryDNA does offer an opt-in/opt-out feature for sharing information for research purposes when users first sign up, though it is rather difficult to find. Clearly, these companies value their ability to share or sell genetic information.<br />
<br />
Since the previously mentioned case of the Golden State Killer, a public debate regarding the release of DNA to law enforcement has emerged. Currently, websites like Ancestry.com and 23andMe have been employed by law enforcement to aid criminal investigations. While most users of these genealogy services join with simple intentions, discover simple genetic/heritage data, they are often unaware that they are additionally making their DNA accessible for reference by law enforcement. Further, the conclusions law enforcement officials are able to draw and the actions they're able to take based on partial genetic matches found remains unclear. <ref> Carolyn Crist, “Experts outline ethics issues with use of genealogy DNA to solve crimes” Reuters, 1 June. 2018, https://www.reuters.com/article/us-health-ethics-genealogy-dna/experts-outline-ethics-issues-with-use-of-genealogy-dna-to-solve-crimes-idUSKCN1IX5O6.</ref><br />
<br />
====Reliability====<br />
Ancestry data platforms are, in part, only as reliable as their customers. Incorrect data provided by a user can consequently affect a platform's data and its analysis and relationships to other users. <br />
Moreover, some platforms choose not disclose the information in their databases to their partners. The lack of transparency might indicate a lack of accuracy in the platform's data. Partners and users should remain skeptic of the results they receive. <ref>Royal, Charmaine D., et al. “Inferring Genetic Ancestry: Opportunities, Challenges, and Implications.” The American Journal of Human Genetics, vol. 86, no. 5, 2010, pp. 661–673., doi:10.1016/j.ajhg.2010.03.011. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869013/<br />
</ref>. Just with most things on the internet, the results are not 100% factual and should be taken with a grain of salt. The results are, however, a good starting point from which a customer could go on to look into their own genealogy and family tree because although the results are not always accurate, they can be realy close..<br />
<br />
== Ethics ==<br />
Ethical questions about ancestral data can arise due to the uncertainty that exists regarding how consumer data gets handled and how secure that data actually is. Without regulation, there are a number of ways in which companies can use customer's ancestral data for financial or market gain. In some cases, the incentive for large corporations to take advantage of user's data for financial gain poses some ethical dilemmas, primarily with in the case of maintaining user privacy.<br />
<br />
=== Utility of Genetic Data ===<br />
Genetic modeling companies, like 23andMe, promote their services to decipher individuals' genetic codes for healthcare related services, and to better understand individual biological functions. 23andMe offers [https://www.23andme.com/dna-health-ancestry/ Health+Ancestry] product package, which aside from providing ancestral genealogical information, provides information on "Health Predispositions," "Wellness," and "Traits." This packaged service will provide information on "how your genetics can influence your chance of developing certain health conditions," "how your genes play a role in your wellbeing and lifestyle choices," and " how your DNA influences your facial features, taste, smell, and other traits." <ref> "Find out what your DNA says about your health, traits and ancestry," 23andMe, https://www.23andme.com/dna-health-ancestry/ </ref> Genetic modeling advertising implies that it can provide personalized healthcare and behavioral analysis based on the genetic data it collects on its consumers. A [http://www.ox.ac.uk/news/2014-07-25-82-our-dna-‘functional’ 2014 Oxford University study] has found that only 8.2% of human DNA has any functionality. That implies that over 90% of human DNA has no functional role to play in human biology. Much of that DNA, according to the study, is simply genetic "baggage" that is carried over from human to human throughout generations. Genetic modeling companies that market individualized information on healthcare and traits, may be deceiving consumers by claiming to offer falsely overly-personalized products. <br />
<br />
=== Health Implications ===<br />
23andMe is one of the leading companies in online ancestry data with an aim that goes beyond processing customer's DNA for ancestry data; it also analyzes DNA to create insightful health reports for each customer's personal genome. The main DNA tests done through 23andMe provide guidance to the customer through the means of dietary suggestions or the restrictions of certain foods and valuable insight about the increased potential for disease risk within the customer's DNA. In a recent article in The Scientist<ref>Loike, John. “Opinion: Consumer DNA Testing Is Crossing into Unethical Territories.” The Scientist Magazine®, 16 Aug. 2018, [http://www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650 www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650].</ref>, Prof. John Loike claims that some of these DNA tests are not as accurate as they are perceived to be. Loike supports this claim by pointing out that 23andMe DNA test only account for 3 of the most common BCRA mutations, the mutations that are commonly used to predict breast cancer. Although 23andMe has DNA testing that addresses the 3 most common mutations, there are over 1000 BCRA mutations that a typical genealogy lab would test for. Some insurance companies are even curious to know whether or not you've taken genetics or DNA test because then they could know if later down the line you could have a genetic condition or illness, that in the end will cost the company more money.<br />
<br />
=== Paternity Tests ===<br />
[[File:madlads.jpg|left|thumbnail|A reddit post from April 19, 2019 shows a user suggesting the family of an unknowingly adopted child buy DNA test kits "and watch the world burn."]]<br />
Hello, I'm Patrick<br />
<br />
On television shows such as the American talk show ''Maury'', couples are brought on to dispute and either verify or disprove the paternity for fatherless children.<ref>The Maury Show. http://www.mauryshow.com/</ref> This may yield surprising results, such as a father with African ancestry having fair-skinned children. With DNA tests, people can be humiliated in public and sensationalize ancestry that is otherwise information people would keep more private. This brings up ethical issues of whether or not these tests should be used for public display and theatrics because of how embarrassing they can be for individuals.<br />
<br />
Reddit has a subreddit called r/23andMe that is dedicated to discussing users' test results when they come back from 23andMe DNA test kits. While this has fostered fruitful conversation and better information amongst users, the subreddit has also turned into a part-time hub for stories of DNA test kits tearing families apart. The subreddit regularly features stories of users whose tests have revealed infidelity, untold adoption, and other issues that cause rifts amongst families.<ref>Reddit r/23 and me, https://www.reddit.com/r/23andme/</ref><br />
<br />
=== White Supremacy ===<br />
One of the many claims made by rising white supremacy groups suggests that possessing pure European ancestry is the mark of superiority, and many individuals within these groups have used ancestral DNA testing as a form of validation in establishing their connection to their perceived superior ancestry. In some cases, white supremacists get results that suggest fully white European ancestry and they react with relief and celebration. Other white supremacists have taken DNA tests only to find out that they're not "pure" white, which causes them to generally discount the test results instead of re-evaluating their views on genetic hierarchies. They usually attribute non-white results to be a statistical error or affirm that family trees are the only evidence needed to prove white ancestry. Some extreme reactions include accusing Jewish people of conspiring to sabotage the DNA test results.<ref>Akpan, Nsikan. How white supremacists respond when their DNA says they're not "white." 20 Aug 2017. PBS News. https://www.pbs.org/newshour/science/white-supremacists-respond-genetics-say-theyre-not-white</ref> This is ethically challenging as these tests by nature are not always accurate, and can push forth ideas and interpretations that are false. In any case, DNA tests may create reason for hate-based groups to spread their ideologies.<br />
<br />
=== Privacy Implications ===<br />
Leading ICT ethicist Luciano Floridi argues that the right to privacy is the right to a renewable identity.<ref>Floridi, L. Ethics Inf Technol (2005) 7: 185. https://doi.org/10.1007/s10676-006-0001-7</ref> A notion that is contradicted by how contemporary ancestry data aggregators sometimes use customers' biological data without their knowledge (as discussed earlier). Recently, Danielle Teuscher had used a sperm donor to have a child and had her daughter and other members of her family take an ancestry test through 23andMe. While Danielle had not intended to find the family of her daughters donor, a woman who was not her mother was linked to her daughter as her Grandmother. Danielle decided to reach out to her donor's mother<ref>Mroz, Jacqueline. “A Mother Learns the Identity of Her Child's Grandmother. A Sperm Bank Threatens to Sue.” The New York Times, The New York Times, 16 Feb. 2019, [http://www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html].</ref>. The Sperm Bank had caught word of her reaching out, which breached their pledge to keep the donor anonymous from Danielle and her daughter, and is pursuing potential legal action. While it is in no part 23andMe's fault, ancestry data has played a large part in the ethical implications of this story as well as others.<br />
<br />
Moreover, ancestry data has shed light on aspects of peoples traits that they weren't even aware that they had. Bob Hutchinson used a DNA test kit wanting to prove his heritage, however, he discovered so much more.<ref>Kolata, Gina. “With a Simple DNA Test, Family Histories Are Rewritten.” The New York Times, The New York Times, 28 Aug. 2017, [http://www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E].</ref> Mr. Hutchinson's mother had never said much of her family other than that they were of Italian and Swedish descent, but through the tests, he learned he had African American roots. Knowing this, he worked to identify some of his relatives, whom had been told to never contact Mr. Hutchinson or his family. While it opened a new world for him, it also broke some of the ethics that the respective families followed, even if they felt they were wrong. <br />
<br />
Similarly, although it is in the interest of companies like 23andMe to keep your data private in order to maintain customer trust and protect the future of their companies, many people are concerned that utilizing these services will eliminate the privacy of your DNA. The main issue is, DNA, like [http://si410wiki.sites.uofmhosting.net/index.php/Iris_Recognition one's Iris] is unique to an individual. And with this uniqueness, comes the threat of duplication or storing the DNA to be used at a later time with the innovative technology of today. Some hold conspiracy theories that ancestry data aggregator companies collect a larger DNA sample than what is needed to perform the basic tests that consumers pay for, and that the rest of the sample is kept in a lab to be used for other experiments outside of the DNA owner's knowledge. It is commonly known that scientists are working on the duplication of DNA, and in a futuristic maybe even dystopian sense - the cloning of a human body. If one considers the numerous samples companies like 23andMe is able to collect, they could potentially be the source of samples for this research and profiting off their customers' DNA more than they are letting on.<br />
<br />
As some philosophers in information technology have suggested, informational privacy may be more effective when focused on protecting data related to users' self-identity<ref>Floridi, L., The Fourth Revolution: How the Infosphere is Reshaping Human Reality, Privacy, Oxford University Press, 2014, 101-128.</ref>. The DNA collected by companies like 23andMe can reveal vital information to individual's identity. Heritage, family, and numerous other unique traits that can be uncovered in ancestry data would likely be thought of as a part of someone's identity. If this information was then used or distributed outside of a customer's control, their privacy would have been seriously breached<ref>Shoemaker, D., Self-exposure and exposure of the self: informational privacy and the presentation of identity, 2009.</ref>.<br />
<br />
Another example of a privacy breach through a genealogical platform is the solving of the Golden State Killer case in April of 2018, where investigators were able to identify the killer by running his DNA through the genealogy platform GEDmatch and identifying his relatives. Although perfectly legal, there was question from the public as to whether this method of data collection should be allowed, since investigators were parsing through the data of people who were not suspects and who were not convicted of anything.<ref name="Guerrini">Guerrini CJ, Robinson JO, Petersen D, McGuire AL (2018) Should police have access to genetic genealogy databases? Capturing the Golden State Killer and other criminals using a controversial new forensic technique. PLoS Biol 16(10): e2006906. https://doi.org/10.1371/journal.pbio.2006906</ref><br />
<br />
===Hacking===<br />
Because of the uniqueness of DNA, hacking is a large concern. In October 2017, there was a MyHeritage breach which leaked over 92 million personal account details<ref>Brown, Kristen V. “Hack of DNA Website Exposes Data From 92 Million Accounts.” Bloomberg.com, Bloomberg, 5 June 2018, [http://www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts].</ref>. The hack became evident due to a private server, which included email addresses and hashed passwords. Because the data is private and MyHeritage understands that the trust with the consumers is extremely coveted, they keep several different servers of data. <ref>"MyHeritage breach leaks millions of account details" Makena Kelly, June 5, 2018. https://www.theverge.com/2018/6/5/17430146/dna-myheritage-ancestry-accounts-compromised-hack-breach </ref> Although MyHeritage customers passwords were leaked, their users's account contents were not, but this breach is evidence of the immense privacy concerns associated with Ancestry data.<br />
<br />
===Religious Affiliation of Ancestry Data Services===<br />
A separate potential conflict of interest that many users may not consider relates to the religious affiliations of many population genealogy databases. Namely, FamilyHeritage, founded in Salt Lake City, Utah, is sponsored by the Church of Jesus Christ Latter-day Saints, or the Mormon Church. Though FamilyHeritage offers a widely used service whose user base extends across 70 countries,<ref> "“FamilySearch.” Wikipedia, Wikimedia Foundation, 7 Apr. 2019, [http://en.wikipedia.org/wiki/FamilySearch en.wikipedia.org/wiki/FamilySearch]."</ref> the founding motive to track ancestral data stems from the belief that people can be reunited in an afterlife. Historically, the LDS Church has collected information on the deceased in order to eternally join families through a temple ceremony.<ref>"“Genealogy Is Important to Mormons Because They Believe in Eternal Families.” Www.mormonnewsroom.org, The Church of Jesus Christ of Latter-Day Saints, 23 May 2011, [http://www.mormonnewsroom.org/topic/genealogy www.mormonnewsroom.org/topic/genealogy]."</ref> Similarly, Ancestry.com, founded in Lehi, Utah by two Brigham Young University (BYU) graduates, also has clear roots in the Mormon church.<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com en.wikipedia.org/wiki/Ancestry.com]."</ref> In fact, Ancestry.com's own records emphasize the intent for users to determine the religious affiliations of their deceased ancestors, and their website offers a "Church Histories & Records" search engine to allow users to do so.<ref>"“Using Religious Records.” Ancestry.com, Ancestry.com, [http://www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf]."</ref><ref>"Church Histories &amp; Records, [http://www.ancestry.com/search/categories/dir_church/ www.ancestry.com/search/categories/dir_church/]."</ref> Though FamilyHeritage and Ancestry.com extend their services to the general public and not just those inside the Mormon Church, religion can divide individuals as often as it unites them, and thus some users may find using an ancestry data cite sponsored by the church to be problematic.<br />
<br />
===Accuracy and the Applied Implication of Self-Identity===<br />
<br />
Ancestry DNA tests are popularly believed to reveal the regions in which our genetic makeup comes from. Results are generally given by a breakdown of our genetic makeup with regional percentages an individual’s DNA derives from. This method is adequate to provide an overview summary of an individual, however, it misleads the consumer’s understanding of what the information means.<br />
<br />
The accuracy of an individual’s results is evident when taking the same individual between different companies. Although closely related, the results won’t have the same percentages and will even include new origins of one’s genetic makeup. This is from the result of discrepancies between companies’ different DNA databases<ref>Rutherford, Adam. “How Accurate Are Online DNA Tests?” Scientific American, 15 Oct. 2018, www.scientificamerican.com/article/how-accurate-are-online-dna-tests/.</ref>. Different sources of data and privately held sample collections have contributed to discrepancies amongst all ancestry test companies. They also don’t analyze the whole strand of DNA, but target locations that most likely contribute to the distinguishability between one individual to another. Humans have around 3 billion base pairs in our genetic code, however, 99.9% of these pairs are identical to everyone. It’s from the remaining 1% of our genetic code where companies can distinguish the qualities that reveal our ancestral past. These unique identifiers are referred to as [https://en.wikipedia.org/wiki/Single-nucleotide_polymorphism Single-Nucleotide Polymorphisms] or SNPs. Depending on the company used to analyze an individual’s DNA sample, different SNPs will contribute to the results while others may be ignored<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>.<br />
<br />
Error exists in the analysis of an individual’s genetic makeup by a misinterpretation by the consumer. The percentages given in the result don’t represent the proportionality of the DNA but also an inherent variance of likelihood with the results given. Consideration must be made that genetic makeup isn’t limited by the borders of countries or regions. Particular SNP arrangements can exist everywhere in the world, however, can have higher concentrations within a region of the world. The results are a probability with a margin of error, and shouldn’t be viewed as completely accurate<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>. [https://www.23andme.com/?mdc1=true 23andMe] even includes a confidence slider to illustrate results based on certainty. If this slider is moved toward more confident, the results become increasingly vague.<br />
<br />
This misinterpretation of data has influenced individuals to reconsider their familial past to the extent of impacting their self-identity. Someone unfamiliar with distant regions tied with their DNA makeup may assume roles based on stereotypes. These results can impact the identity of their consumers from probabilities and misinterpretations. When results are inaccurate, it arises a question of ethics by considering the role these companies make in influencing individuals’ self-perspective to cause an alteration in their self-identity.<br />
<br />
==Conclusion==<br />
As demonstrated, ancestry data has been a catalyst for many different ethical concerns. Whether it has been used to interpret medical data and allowing law enforcement access to our data, to circumnavigating the privacy rules of sperm banks, it had caused some unsettling feelings for many people. It is clear that in some instances the information is used to uphold the moral good, but the underlying concerns demand more discussion. One way to ensure people's privacy, proposed by Kathleen Wallace, is to use the idea of traits, such as gender, age, Social Security Number, and more as the defining qualities of that make up our anonymity. When some of these traits are hidden from public knowledge, these people are considered to be anonymous to an extent.<ref>Wallace, K.A. Ethics and Information Technology (1999) 1: 21. https://doi.org/10.1023/A:1010066509278</ref> Another way might be to have more regulations on how companies should state clearly the possible ways they will use the data besides genealogy purpose and how they should ask informed permissions before actually using the data. Senator Chuck Schumer warns that privacy concerns are not made clear enough to consumers.<ref>"US Senator Calls on FTC to Investigate DNA Ancestry Companies" Seth Augenstein, November 27, 2017. https://www.forensicmag.com/news/2017/11/us-senator-calls-ftc-investigate-dna-ancestry-companies</ref> Consumers most private information could potentially be sold to third parties, requiring an investigation by the federal trade commission.<br />
<br />
==See Also==<br />
*[[Genealogy platforms]]<br />
*[[DNA Testing]]<br />
<br />
== References ==<br />
<br />
[[Category:2019New]]<br />
[[Category:Information Ethics]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Ancestry_data&diff=91501Ancestry data2021-02-11T18:18:00Z<p>WikiSysop: Reverted edits by Raybart (talk) to last revision by Ajamalud</p>
<hr />
<div>{{Nav-Bar|Topics##}}<br><br />
<br />
[[File:Britishfamilytree.png|400px|thumb|right|The current family tree of British Royalty]]<br />
'''Ancestry data''' is collected information tracking an individual's [[Wikipedia:Lineage_(genetic)|family lineage]] to identify their [[Wikipedia:Ethnic_origin|ethnic origin]], heritage, [[Wikipedia:Place_of_birth|place of birth]], and relatives. For thousands of years, records of [[Wikipedia:ancestor|ancestry]] data have been kept to clearly define concepts of birthright and familial succession throughout both modern and ancient civilizations.<br />
<br />
Modern day technology has changed the processes of collecting and interpreting ancestry data. Companies like [https://www.23andme.com/ 23andMe] and [https://www.ancestrydna.com/kits/?s_kwcid=ancestors+dna&gclid=Cj0KCQjw1pblBRDSARIsACfUG13YNht7Foyirz_we6B_loNzZBh9I8RnQOcmxOL1a5TtUboVHYU7fgQaAikDEALw_wcB&gclsrc=aw.ds&o_xid=79108&o_lid=79108&o_sch=Paid+Search+Non+Brand AncestryDNA] offer full ethnic background maps and potential [[Wikipedia:family_tree|family tree]] links to the general public. This thriving [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer (DTC)] ancestry data industry originates from a fascination with the concept of human identity and origin. The industry's success has lead to a new form of personalized medicines and treatment plans based on genetic makeup. As of 2014, [[Wikipedia:Genealogy|genealogy]] was a 2-billion-dollar industry, and is continuing to grow.<ref> “The Genealogy Industry: $2 Billion–and Growing!” Genealogy Gems, 11 Dec. 2014, [http://lisalouisecooke.com/2014/12/11/genealogy-industry-growing lisalouisecooke.com/2014/12/11/genealogy-industry-growing]/.</ref> These new companies have made it affordable and efficient for the general public to discover more about themselves in terms of ancestry. However, as ancestry data is applied to health care solutions, a range of ethical problems, including racial supremacy, health, and privacy, emerges. <br />
<br />
== Modern Day Use and Influence of Technology ==<br />
Since the discovery of the [[Wikipedia:Nucleic_acid_double_helix|double helix]] by [[Wikipedia:Francis_Crick|Francis Crick]] and [[Wikipedia:James_Watson|James Watson]] in 1935, scientists have worked tirelessly to better understand DNA, the [[Wikipedia:human_genome|human genome]] and its countless implications. The [[Wikipedia:Human_Genome_Project|Human Genome Project]], the first full human DNA sequence in history, cost almost $3 billion.<ref>"The Human Genome Project Completion: Frequently Asked Questions" https://www.genome.gov/11006943/human-genome-project-completion-frequently-asked-questions/</ref> In contrast with today's technological advancement, an individual can get their DNA sequenced and analyzed for personal use through a variety of popular vendors for under $100. <br />
<br />
=== How Ancestry Data Works Today ===<br />
[[File:23.jpg|1000px|thumb|left|23andMe DNA testing kit, along with its instructions]]<br />
With the popularization of discovering familial lineage online, it has become incredibly simple for a person to receive their ancestry data. Interested parties simply purchase a kit from a direct-to-consumer DNA testing company, such as [[Wikipedia:23andMe|23andMe]] or any of the other 25 major competitors, follow the [[Wikipedia:DNA|DNA]] harvesting directions (i.e. spit into a small plastic tube), and send the completed test kit back to the company. Once a DNA sample is received, the company will process and sequence the DNA. In the case of 23andMe, this process takes 3-5 weeks.<ref> "When Will My Results Be Ready?" 23andMe, https://customercare.23andme.com/hc/en-us/articles/202904740-When-will-my-results-be-ready- </ref> With the help of trained professionals and [[algorithms]], DNA sequencing can provide information that changes lives. Robin Smith, head of 23andMe’s Ancestry program explains how the algorithm works: "it takes an entire genome and chunks it up...It takes little pieces, and for each piece, it compares it against the reference data set. It compares it against British; it compares it against West African; it goes through the entire list, and it spits out a probability for [where that piece of DNA came from]" <ref>Letzter, Rafi. “How Do DNA Ancestry Tests Really Work?” LiveScience, Purch, 4 June 2018, [http://www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html www.livescience.com/62690-how-dna-ancestry-23andme-tests-work.html].</ref>. DNA sequencing also has the capability to speculate potential relatives based on near complete matches of DNA, and boasts very accurate results for those considered “Close Family” or “First Cousins”. <br />
<br />
Overall, technological advancements have made it relatively easy for the general public to access their genetic identity. With few barriers to entry, a relatively low cost, and a quick turnaround, access to ancestral data is quite easy. However, the modern applications of genetic data have become far more contentious when considering the ethical implications of a large quantity of ancestral data.<br />
<br />
===Genealogy Companies===<br />
There are many different [https://en.wikipedia.org/wiki/Genetic_testing#Direct-to-consumer_genetic_testing direct-to-consumer personal genomic websites] that allow individuals to receive a report of their ancestry data. Distinguishable characteristics among these genealogy databases aside, the sheer variety in platforms and the user base they each hold demonstrates the true prevalence of ancestry data in modern life. Different sites use different reference databases to compare your genetic information. Thus, results may differ between different companies from the same genetic information.<br />
<br />
{| class="wikitable"<br />
|-<br />
! Company !! Description<br />
|-<br />
| [https://www.ancestry.com/ Ancestry] || One of the most popular direct-to-consumer personal genomic websites with 3 million paying members that offers access to 10 billion historical reports<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com. en.wikipedia.org/wiki/Ancestry.com]"</ref>.<br />
|-<br />
| [https://www.23andme.com/?mdc1=true 23 and Me] || A DNA test kit service that provides over 125 reports in an individual's ancestry, health predispositions, wellness, carrier status, and traits <ref>23andMe. “DNA Genetic Testing &amp; Analysis.” 23andMe, [https://www.23andme.com/23andMe]</ref>.<br />
|-<br />
| [https://www.familysearch.org/en/ Family Search] || Free of charge genealogy database service that offers comprehensive sequencing and analysis of personal DNA.<br />
|-<br />
| [https://www.myheritage.com/?utm_source=ppc_google&utm_medium=cpc&utm_campaign=mh_search_us_en_des_mul_exact_myheritage&utm_content=289683747520&utm_term=my+heritage&tr_camp_id=344023924&tr_ad_group=myheritage&tr_ag_id=24241928044&tr_placement=&tr_device=c&tr_account=904-055-9108&keyword=&tr_size=&recordtype=&recordlocation=&gclid=EAIaIQobChMIz-_TxKzm4QIVgbbACh1Qag_hEAAYASAAEgKONPD_BwE My Heritage] || Israel-based company that provides a DNA test kit service that is delivered to consumers' homes and processed quicker than any other service option. The company supports 92 million users worldwide<ref>"“MyHeritage.” Wikipedia, Wikimedia Foundation, 18 Apr. 2019, [http://en.wikipedia.org/wiki/MyHeritage. en.wikipedia.org/wiki/MyHeritage]"</ref>. [https://www.geni.com Geni], another ancestry company was bought by MyHeritage in 2012<ref>"Geni Is Joining The MyHeritage Family!"https://www.geni.com/blog/geni-is-joining-the-myheritage-family-378424.html</ref>. Geni helps connect people with ancestors, or make ancestral connections through generational family trees. The platform allows users to find and connect to relatives that might belong to their heritage or family tree, work with them, while organizing their relations. Their goal is to make one large tree called the World Family Tree.<ref>Rick Crume, "Quick Guide to the Geni Family Tree Website" https://www.familytreemagazine.com/premium/geni-quick-guide/, Feb 23 2015 </ref><br />
|-<br />
| [https://www.archives.com/genealogy/dna-testing.html Archives] || A service that provides more indebt genealogical data from users who are already familiar with their genealogy.<br />
|-<br />
| [https://www.findmypast.com/?ds_kid=43700029751096390&gclid=EAIaIQobChMIvej_itHm4QIVkrrACh2i7glpEAAYASAAEgKaTfD_BwE&gclsrc=aw.ds FindMyPast] || Provides genealogical services to individuals with little to no personal genetics-related knowledge, such as individuals who have been adopted. Results are returned through easily digestible and enable further action for research <ref>Top Ten Reviews. “https://www.toptenreviews.com/services/home/best-genealogy-websites/.</ref>.<br />
|-<br />
<br />
|}<br />
<br />
=== Benefits of Ancestry Data ===<br />
Hi, I am testing this. Whether it be learning about one's genetic predispositions to medical conditions, or discovering your family history, accessing one's ancestry data offers numerous potential benefits. For instance, 23andMe offers over 100 tests that provide consumers with a variety of personalized health data. You can discover what dominant traits will be passed down to offspring, what genes do and do not affect your health, and what foods you should and shouldn’t eat <ref>23andMe. “Our Health + Ancestry DNA Service.” 23andMe, [http://www.23andme.com/dna-health-ancestry/ www.23andme.com/dna-health-ancestry/].</ref>. 23andMe has used their ancestry and DNA databases to aid in research as well. A study conducted by Dr. Abraham Palmer and his team at the University of California - San Diego School of Medicine used over 20,000 consenting 23andMe users to determine that there is a connection between impulsiveness and drug use in humans <ref>23andMe. “Genetic Study of Impulsiveness Reveals Associations with Drug Use.” 23andMe Blog, 4 Feb. 2019, [http://blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/ blog.23andme.com/23andme-research/genetic-study-of-impulsiveness-reveals-associations-with-drug-use/].</ref>. Other studies have identified many genes related to depression and other mental illnesses. Though it is frowned upon<ref> “What Are the Benefits and Risks of Direct-to-Consumer Genetic Testing? - Genetics Home Reference - NIH.” U.S. National Library of Medicine, National Institutes of Health, ghr.nlm.nih.gov/primer/dtcgenetictesting/dtcrisksbenefits.</ref> by healthcare professionals to make medical decisions based off of ancestry data - without input from genetic counselors, physicians, etc. - purchasing ancestry data services is less expensive than hiring a professional genealogist. Consumers are able to exploit the power of personal genomics and gain at least some insight regarding their lineage, family history, and/or genetic predispositions. <br />
<br />
Moreover, consumers may find distant (or not-so-distant) relatives through ancestry databases. These services provide consumers with a unique opportunity to form connections with living biological family members who would have otherwise remained unknown. There are also many consumers of ancestry data who simply employ these services because they enjoy examining, learning about and/or discovering their genealogy - almost as if it were a hobby.<br />
<br />
In recent popular culture, DNA testing databases were leveraged in order to capture the [[Wikipedia:Golden_State_Killer|Golden State Killer]]. Law enforcement used DNA profiles from ancestry sites to catch and identify the killer by first locating his relatives <ref>Romano, Aja. “DNA Profiles from Ancestry Websites Helped Identify the Golden State Killer Suspect.” Vox, Vox, 27 Apr. 2018, [http://www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match www.vox.com/2018/4/27/17290288/golden-state-killer-joseph-james-deangelo-dna-profile-match].</ref>. This is becoming an increasingly common method for law enforcement and forensic science, which has lead to an innovative and more efficient (albeit controversial) way to track down criminals. Furthermore, the same technology that is being used to solve decades-old crimes is being adopted by genealogists to identify victims of crime across the country as well. Currently, Private DNA test kits like Ancestry and 23andMe are closed to law enforcement due to privacy concerns but users can upload their genetic code site to public sites like GEDMatch which law enforcement officials are able to access <ref name = "Anguiano"> Anguiano, Barbara. “Using Genetic Genealogy To Identify Unknown Crime Victims, Sometimes Decades Later.” NPR, NPR, 8 Jan. 2019, www.npr.org/2019/01/08/682925589/using-genetic-genealogy-to-identify-unknown-crime-victims-sometimes-decades-late.</ref><br />
<br />
=== Consequences of Ancestry Data ===<br />
However, as technology expands to allow for the additional applications of ancestry data, numerous consequences have emerged and been brought to public attention.<br />
Just as Facebook and other social media platforms sell and share user data with their partners, 23andMe has been known to do the same with pharmaceutical companies (e.g., a $300 million-dollar deal with GlaxoSmithKline <ref>Martin, Nicole. “How DNA Companies Like Ancestry And 23andMe Are Using Your Genetic Data.” Forbes, Forbes Magazine, 5 Dec. 2018, [http://www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189 www.forbes.com/sites/nicolemartin1/2018/12/05/how-dna-companies-like-ancestry-and-23andme-are-using-your-genetic-data/#5d3ba70a6189].</ref>). Direct-to-consumer companies disclose extremely personal (and private) genetic data to outsider organizations for research purposes, among other reasons. <br />
<br />
Further, once the DNA data and ancestry data has been processed, it is almost impossible for the data to be removed from the site. Unintentional sharing is also very common which often generates discomfort or distrust among customers. <ref>Brodwin, Erin. “DNA-Testing Company 23andMe Has Signed a $300 Million Deal with a Drug Giant. Here's How to Delete Your Data If That Freaks You out.” Business Insider, Business Insider, 25 July 2018, [http://www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7 www.businessinsider.com/dna-testing-delete-your-data-23andme-ancestry-2018-7].</ref>. Even though all genetic information is anonymized - and (apparently) cannot be traced back to its owner - it should be protected as if it were each customer's social security number. As technological innovation continues to evolve, one can only imagine the ways in which genetic information will be introduced into everyday life. If today's consumers remain naive to the poor management of genetic information, and continue to hand over personal genetic information to companies that seek to profit off of it - said consumers are putting themselves at serious risk. There is greater potential that their personal genetic information will be leveraged against them. <br />
<br />
Norman Mooradian’s states in his paper “Importance of privacy revisited”, that people should be able to control or restrict the access of information <ref>Mooradian, Norman. “The Importance of Privacy Revisited.” Ethics and Information Technology, vol. 11, no. 3, 14 July 2009, pp. 163–174., doi:10.1007/s10676-009-9201-2.</ref>. To combat the potential consequences associated with the ways in which ancestry data is utilized today, it is important to give people the power to decide where their genetic makeup goes. Moreover, personal genomic companies are not sufficiently transparent regarding their practices and/or treatment of consumer data: individual consumers are likely not fully aware of the autonomy they are giving up when they submit their DNA to these companies. Effectively, consumers are relinquishing their right to "control or restrict" (per Mooradian) their own genetic information. AncestryDNA does offer an opt-in/opt-out feature for sharing information for research purposes when users first sign up, though it is rather difficult to find. Clearly, these companies value their ability to share or sell genetic information.<br />
<br />
Since the previously mentioned case of the Golden State Killer, a public debate regarding the release of DNA to law enforcement has emerged. Currently, websites like Ancestry.com and 23andMe have been employed by law enforcement to aid criminal investigations. While most users of these genealogy services join with simple intentions, discover simple genetic/heritage data, they are often unaware that they are additionally making their DNA accessible for reference by law enforcement. Further, the conclusions law enforcement officials are able to draw and the actions they're able to take based on partial genetic matches found remains unclear. <ref> Carolyn Crist, “Experts outline ethics issues with use of genealogy DNA to solve crimes” Reuters, 1 June. 2018, https://www.reuters.com/article/us-health-ethics-genealogy-dna/experts-outline-ethics-issues-with-use-of-genealogy-dna-to-solve-crimes-idUSKCN1IX5O6.</ref><br />
<br />
====Reliability====<br />
Ancestry data platforms are, in part, only as reliable as their customers. Incorrect data provided by a user can consequently affect a platform's data and its analysis and relationships to other users. <br />
Moreover, some platforms choose not disclose the information in their databases to their partners. The lack of transparency might indicate a lack of accuracy in the platform's data. Partners and users should remain skeptic of the results they receive. <ref>Royal, Charmaine D., et al. “Inferring Genetic Ancestry: Opportunities, Challenges, and Implications.” The American Journal of Human Genetics, vol. 86, no. 5, 2010, pp. 661–673., doi:10.1016/j.ajhg.2010.03.011. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869013/<br />
</ref>. Just with most things on the internet, the results are not 100% factual and should be taken with a grain of salt. The results are, however, a good starting point from which a customer could go on to look into their own genealogy and family tree because although the results are not always accurate, they can be realy close..<br />
<br />
== Ethics ==<br />
Ethical questions about ancestral data can arise due to the uncertainty that exists regarding how consumer data gets handled and how secure that data actually is. Without regulation, there are a number of ways in which companies can use customer's ancestral data for financial or market gain. In some cases, the incentive for large corporations to take advantage of user's data for financial gain poses some ethical dilemmas, primarily with in the case of maintaining user privacy.<br />
<br />
=== Utility of Genetic Data ===<br />
Genetic modeling companies, like 23andMe, promote their services to decipher individuals' genetic codes for healthcare related services, and to better understand individual biological functions. 23andMe offers [https://www.23andme.com/dna-health-ancestry/ Health+Ancestry] product package, which aside from providing ancestral genealogical information, provides information on "Health Predispositions," "Wellness," and "Traits." This packaged service will provide information on "how your genetics can influence your chance of developing certain health conditions," "how your genes play a role in your wellbeing and lifestyle choices," and " how your DNA influences your facial features, taste, smell, and other traits." <ref> "Find out what your DNA says about your health, traits and ancestry," 23andMe, https://www.23andme.com/dna-health-ancestry/ </ref> Genetic modeling advertising implies that it can provide personalized healthcare and behavioral analysis based on the genetic data it collects on its consumers. A [http://www.ox.ac.uk/news/2014-07-25-82-our-dna-‘functional’ 2014 Oxford University study] has found that only 8.2% of human DNA has any functionality. That implies that over 90% of human DNA has no functional role to play in human biology. Much of that DNA, according to the study, is simply genetic "baggage" that is carried over from human to human throughout generations. Genetic modeling companies that market individualized information on healthcare and traits, may be deceiving consumers by claiming to offer falsely overly-personalized products. <br />
<br />
=== Health Implications ===<br />
23andMe is one of the leading companies in online ancestry data with an aim that goes beyond processing customer's DNA for ancestry data; it also analyzes DNA to create insightful health reports for each customer's personal genome. The main DNA tests done through 23andMe provide guidance to the customer through the means of dietary suggestions or the restrictions of certain foods and valuable insight about the increased potential for disease risk within the customer's DNA. In a recent article in The Scientist<ref>Loike, John. “Opinion: Consumer DNA Testing Is Crossing into Unethical Territories.” The Scientist Magazine®, 16 Aug. 2018, [http://www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650 www.the-scientist.com/news-opinion/opinion--consumer-dna-testing-is-crossing-into-unethical-territories-64650].</ref>, Prof. John Loike claims that some of these DNA tests are not as accurate as they are perceived to be. Loike supports this claim by pointing out that 23andMe DNA test only account for 3 of the most common BCRA mutations, the mutations that are commonly used to predict breast cancer. Although 23andMe has DNA testing that addresses the 3 most common mutations, there are over 1000 BCRA mutations that a typical genealogy lab would test for. Some insurance companies are even curious to know whether or not you've taken genetics or DNA test because then they could know if later down the line you could have a genetic condition or illness, that in the end will cost the company more money.<br />
<br />
=== Paternity Tests ===<br />
[[File:madlads.jpg|thumbnail|A reddit post from April 19, 2019 shows a user suggesting the family of an unknowingly adopted child buy DNA test kits "and watch the world burn."]]<br />
Hello, I'm Patrick<br />
<br />
On television shows such as the American talk show ''Maury'', couples are brought on to dispute and either verify or disprove the paternity for fatherless children.<ref>The Maury Show. http://www.mauryshow.com/</ref> This may yield surprising results, such as a father with African ancestry having fair-skinned children. With DNA tests, people can be humiliated in public and sensationalize ancestry that is otherwise information people would keep more private. This brings up ethical issues of whether or not these tests should be used for public display and theatrics because of how embarrassing they can be for individuals.<br />
<br />
Reddit has a subreddit called r/23andMe that is dedicated to discussing users' test results when they come back from 23andMe DNA test kits. While this has fostered fruitful conversation and better information amongst users, the subreddit has also turned into a part-time hub for stories of DNA test kits tearing families apart. The subreddit regularly features stories of users whose tests have revealed infidelity, untold adoption, and other issues that cause rifts amongst families.<ref>Reddit r/23 and me, https://www.reddit.com/r/23andme/</ref><br />
<br />
=== White Supremacy ===<br />
One of the many claims made by rising white supremacy groups suggests that possessing pure European ancestry is the mark of superiority, and many individuals within these groups have used ancestral DNA testing as a form of validation in establishing their connection to their perceived superior ancestry. In some cases, white supremacists get results that suggest fully white European ancestry and they react with relief and celebration. Other white supremacists have taken DNA tests only to find out that they're not "pure" white, which causes them to generally discount the test results instead of re-evaluating their views on genetic hierarchies. They usually attribute non-white results to be a statistical error or affirm that family trees are the only evidence needed to prove white ancestry. Some extreme reactions include accusing Jewish people of conspiring to sabotage the DNA test results.<ref>Akpan, Nsikan. How white supremacists respond when their DNA says they're not "white." 20 Aug 2017. PBS News. https://www.pbs.org/newshour/science/white-supremacists-respond-genetics-say-theyre-not-white</ref> This is ethically challenging as these tests by nature are not always accurate, and can push forth ideas and interpretations that are false. In any case, DNA tests may create reason for hate-based groups to spread their ideologies.<br />
<br />
=== Privacy Implications ===<br />
Leading ICT ethicist Luciano Floridi argues that the right to privacy is the right to a renewable identity.<ref>Floridi, L. Ethics Inf Technol (2005) 7: 185. https://doi.org/10.1007/s10676-006-0001-7</ref> A notion that is contradicted by how contemporary ancestry data aggregators sometimes use customers' biological data without their knowledge (as discussed earlier). Recently, Danielle Teuscher had used a sperm donor to have a child and had her daughter and other members of her family take an ancestry test through 23andMe. While Danielle had not intended to find the family of her daughters donor, a woman who was not her mother was linked to her daughter as her Grandmother. Danielle decided to reach out to her donor's mother<ref>Mroz, Jacqueline. “A Mother Learns the Identity of Her Child's Grandmother. A Sperm Bank Threatens to Sue.” The New York Times, The New York Times, 16 Feb. 2019, [http://www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html www.nytimes.com/2019/02/16/health/sperm-donation-dna-testing.html].</ref>. The Sperm Bank had caught word of her reaching out, which breached their pledge to keep the donor anonymous from Danielle and her daughter, and is pursuing potential legal action. While it is in no part 23andMe's fault, ancestry data has played a large part in the ethical implications of this story as well as others.<br />
<br />
Moreover, ancestry data has shed light on aspects of peoples traits that they weren't even aware that they had. Bob Hutchinson used a DNA test kit wanting to prove his heritage, however, he discovered so much more.<ref>Kolata, Gina. “With a Simple DNA Test, Family Histories Are Rewritten.” The New York Times, The New York Times, 28 Aug. 2017, [http://www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E www.nytimes.com/2017/08/28/science/dna-tests-ancestry.html?module=inline%3C%2Fref%3E].</ref> Mr. Hutchinson's mother had never said much of her family other than that they were of Italian and Swedish descent, but through the tests, he learned he had African American roots. Knowing this, he worked to identify some of his relatives, whom had been told to never contact Mr. Hutchinson or his family. While it opened a new world for him, it also broke some of the ethics that the respective families followed, even if they felt they were wrong. <br />
<br />
Similarly, although it is in the interest of companies like 23andMe to keep your data private in order to maintain customer trust and protect the future of their companies, many people are concerned that utilizing these services will eliminate the privacy of your DNA. The main issue is, DNA, like [http://si410wiki.sites.uofmhosting.net/index.php/Iris_Recognition one's Iris] is unique to an individual. And with this uniqueness, comes the threat of duplication or storing the DNA to be used at a later time with the innovative technology of today. Some hold conspiracy theories that ancestry data aggregator companies collect a larger DNA sample than what is needed to perform the basic tests that consumers pay for, and that the rest of the sample is kept in a lab to be used for other experiments outside of the DNA owner's knowledge. It is commonly known that scientists are working on the duplication of DNA, and in a futuristic maybe even dystopian sense - the cloning of a human body. If one considers the numerous samples companies like 23andMe is able to collect, they could potentially be the source of samples for this research and profiting off their customers' DNA more than they are letting on.<br />
<br />
As some philosophers in information technology have suggested, informational privacy may be more effective when focused on protecting data related to users' self-identity<ref>Floridi, L., The Fourth Revolution: How the Infosphere is Reshaping Human Reality, Privacy, Oxford University Press, 2014, 101-128.</ref>. The DNA collected by companies like 23andMe can reveal vital information to individual's identity. Heritage, family, and numerous other unique traits that can be uncovered in ancestry data would likely be thought of as a part of someone's identity. If this information was then used or distributed outside of a customer's control, their privacy would have been seriously breached<ref>Shoemaker, D., Self-exposure and exposure of the self: informational privacy and the presentation of identity, 2009.</ref>.<br />
<br />
Another example of a privacy breach through a genealogical platform is the solving of the Golden State Killer case in April of 2018, where investigators were able to identify the killer by running his DNA through the genealogy platform GEDmatch and identifying his relatives. Although perfectly legal, there was question from the public as to whether this method of data collection should be allowed, since investigators were parsing through the data of people who were not suspects and who were not convicted of anything.<ref name="Guerrini">Guerrini CJ, Robinson JO, Petersen D, McGuire AL (2018) Should police have access to genetic genealogy databases? Capturing the Golden State Killer and other criminals using a controversial new forensic technique. PLoS Biol 16(10): e2006906. https://doi.org/10.1371/journal.pbio.2006906</ref><br />
<br />
===Hacking===<br />
Because of the uniqueness of DNA, hacking is a large concern. In October 2017, there was a MyHeritage breach which leaked over 92 million personal account details<ref>Brown, Kristen V. “Hack of DNA Website Exposes Data From 92 Million Accounts.” Bloomberg.com, Bloomberg, 5 June 2018, [http://www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts www.bloomberg.com/news/articles/2018-06-05/hack-of-dna-website-exposes-data-from-92-million-user-accounts].</ref>. The hack became evident due to a private server, which included email addresses and hashed passwords. Because the data is private and MyHeritage understands that the trust with the consumers is extremely coveted, they keep several different servers of data. <ref>"MyHeritage breach leaks millions of account details" Makena Kelly, June 5, 2018. https://www.theverge.com/2018/6/5/17430146/dna-myheritage-ancestry-accounts-compromised-hack-breach </ref> Although MyHeritage customers passwords were leaked, their users's account contents were not, but this breach is evidence of the immense privacy concerns associated with Ancestry data.<br />
<br />
===Religious Affiliation of Ancestry Data Services===<br />
A separate potential conflict of interest that many users may not consider relates to the religious affiliations of many population genealogy databases. Namely, FamilyHeritage, founded in Salt Lake City, Utah, is sponsored by the Church of Jesus Christ Latter-day Saints, or the Mormon Church. Though FamilyHeritage offers a widely used service whose user base extends across 70 countries,<ref> "“FamilySearch.” Wikipedia, Wikimedia Foundation, 7 Apr. 2019, [http://en.wikipedia.org/wiki/FamilySearch en.wikipedia.org/wiki/FamilySearch]."</ref> the founding motive to track ancestral data stems from the belief that people can be reunited in an afterlife. Historically, the LDS Church has collected information on the deceased in order to eternally join families through a temple ceremony.<ref>"“Genealogy Is Important to Mormons Because They Believe in Eternal Families.” Www.mormonnewsroom.org, The Church of Jesus Christ of Latter-Day Saints, 23 May 2011, [http://www.mormonnewsroom.org/topic/genealogy www.mormonnewsroom.org/topic/genealogy]."</ref> Similarly, Ancestry.com, founded in Lehi, Utah by two Brigham Young University (BYU) graduates, also has clear roots in the Mormon church.<ref>"“Ancestry.com.” Wikipedia, Wikimedia Foundation, 12 Mar. 2019, [http://en.wikipedia.org/wiki/Ancestry.com en.wikipedia.org/wiki/Ancestry.com]."</ref> In fact, Ancestry.com's own records emphasize the intent for users to determine the religious affiliations of their deceased ancestors, and their website offers a "Church Histories & Records" search engine to allow users to do so.<ref>"“Using Religious Records.” Ancestry.com, Ancestry.com, [http://www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf www.ancestrycdn.com/support/us/2016/11/usingreligiousrecords.pdf]."</ref><ref>"Church Histories &amp; Records, [http://www.ancestry.com/search/categories/dir_church/ www.ancestry.com/search/categories/dir_church/]."</ref> Though FamilyHeritage and Ancestry.com extend their services to the general public and not just those inside the Mormon Church, religion can divide individuals as often as it unites them, and thus some users may find using an ancestry data cite sponsored by the church to be problematic.<br />
<br />
===Accuracy and the Applied Implication of Self-Identity===<br />
<br />
Ancestry DNA tests are popularly believed to reveal the regions in which our genetic makeup comes from. Results are generally given by a breakdown of our genetic makeup with regional percentages an individual’s DNA derives from. This method is adequate to provide an overview summary of an individual, however, it misleads the consumer’s understanding of what the information means.<br />
<br />
The accuracy of an individual’s results is evident when taking the same individual between different companies. Although closely related, the results won’t have the same percentages and will even include new origins of one’s genetic makeup. This is from the result of discrepancies between companies’ different DNA databases<ref>Rutherford, Adam. “How Accurate Are Online DNA Tests?” Scientific American, 15 Oct. 2018, www.scientificamerican.com/article/how-accurate-are-online-dna-tests/.</ref>. Different sources of data and privately held sample collections have contributed to discrepancies amongst all ancestry test companies. They also don’t analyze the whole strand of DNA, but target locations that most likely contribute to the distinguishability between one individual to another. Humans have around 3 billion base pairs in our genetic code, however, 99.9% of these pairs are identical to everyone. It’s from the remaining 1% of our genetic code where companies can distinguish the qualities that reveal our ancestral past. These unique identifiers are referred to as [https://en.wikipedia.org/wiki/Single-nucleotide_polymorphism Single-Nucleotide Polymorphisms] or SNPs. Depending on the company used to analyze an individual’s DNA sample, different SNPs will contribute to the results while others may be ignored<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>.<br />
<br />
Error exists in the analysis of an individual’s genetic makeup by a misinterpretation by the consumer. The percentages given in the result don’t represent the proportionality of the DNA but also an inherent variance of likelihood with the results given. Consideration must be made that genetic makeup isn’t limited by the borders of countries or regions. Particular SNP arrangements can exist everywhere in the world, however, can have higher concentrations within a region of the world. The results are a probability with a margin of error, and shouldn’t be viewed as completely accurate<ref>Resnick, Brian. “The Limits of Ancestry DNA Tests, Explained.” Vox, Vox, 12 Feb. 2019, www.vox.com/science-and-health/2019/1/28/18194560/ancestry-dna-23-me-myheritage-science-explainer.</ref>. [https://www.23andme.com/?mdc1=true 23andMe] even includes a confidence slider to illustrate results based on certainty. If this slider is moved toward more confident, the results become increasingly vague.<br />
<br />
This misinterpretation of data has influenced individuals to reconsider their familial past to the extent of impacting their self-identity. Someone unfamiliar with distant regions tied with their DNA makeup may assume roles based on stereotypes. These results can impact the identity of their consumers from probabilities and misinterpretations. When results are inaccurate, it arises a question of ethics by considering the role these companies make in influencing individuals’ self-perspective to cause an alteration in their self-identity.<br />
<br />
==Conclusion==<br />
As demonstrated, ancestry data has been a catalyst for many different ethical concerns. Whether it has been used to interpret medical data and allowing law enforcement access to our data, to circumnavigating the privacy rules of sperm banks, it had caused some unsettling feelings for many people. It is clear that in some instances the information is used to uphold the moral good, but the underlying concerns demand more discussion. One way to ensure people's privacy, proposed by Kathleen Wallace, is to use the idea of traits, such as gender, age, Social Security Number, and more as the defining qualities of that make up our anonymity. When some of these traits are hidden from public knowledge, these people are considered to be anonymous to an extent.<ref>Wallace, K.A. Ethics and Information Technology (1999) 1: 21. https://doi.org/10.1023/A:1010066509278</ref> Another way might be to have more regulations on how companies should state clearly the possible ways they will use the data besides genealogy purpose and how they should ask informed permissions before actually using the data. Senator Chuck Schumer warns that privacy concerns are not made clear enough to consumers.<ref>"US Senator Calls on FTC to Investigate DNA Ancestry Companies" Seth Augenstein, November 27, 2017. https://www.forensicmag.com/news/2017/11/us-senator-calls-ftc-investigate-dna-ancestry-companies</ref> Consumers most private information could potentially be sold to third parties, requiring an investigation by the federal trade commission.<br />
<br />
==See Also==<br />
*[[Genealogy platforms]]<br />
*[[DNA Testing]]<br />
<br />
== References ==<br />
<br />
[[Category:2019New]]<br />
[[Category:Information Ethics]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91450User:WikiSysop2021-02-11T18:01:43Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Zoji La Pass (India).<ref>Kelly Marks, The 12 Scariest and Most Dangerous Roads in the World. Wander Wisdom, July 25, 2020. https://wanderwisdom.com/misc/10-Most-Dangerous-Roads-in-the-World</ref><br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
=My Frank Turner Obsession=<br />
<br />
[[File:Frank Turner March 2020.jpg|left|thumb|200px|Frank Turner rocks!]]<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Frank Turner, ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91449User:WikiSysop2021-02-11T18:01:20Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Zoji La Pass (India).<ref>Kelly Marks, The 12 Scariest and Most Dangerous Roads in the World. Wander Wisdom, July 25, 2020. https://wanderwisdom.com/misc/10-Most-Dangerous-Roads-in-the-World</ref><br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
=My Frank Turner Obsession=<br />
<br />
[[File:Frank Turner March 2020.jpg|left|thumb|200px|Frank Turner rocks!]]<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Frank Turner, ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=File:Frank_Turner_March_2020.jpg&diff=91448File:Frank Turner March 2020.jpg2021-02-11T17:59:53Z<p>WikiSysop: Great Frank Pic.</p>
<hr />
<div>Great Frank Pic.</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Algorithms&diff=91443Algorithms2021-02-11T17:55:02Z<p>WikiSysop: Reverted edits by WikiSysop (talk) to last revision by Nlampa</p>
<hr />
<div>[[File:Algorithm.png|200px|thumb|right]]<br />
{{Nav-Bar|Topics##}}<br><br />
An '''Algorithm''' is defined as a set of precise steps and distinct states used to express the detailed structure of a program or the order of events that occurred in a system <ref>Cormen, Thomas H. et al. (2009). ''Introduction to Algorithms;'. MIT Press.</ref>.Algorithms are involved in many aspects of daily life and in complex computer science concepts. They often use repetition of operations to allow people and machines to execute tasks more efficiently by executing tasks faster and using fewer resources such as memory. On a basic level, an algorithm is a system working through different iterations of a process<ref>Lim, Brian (December 7, 2016). [e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/ "A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life)"] ''e27''.</ref>. They can help turn systematic and tedious tasks into fast, automated processes. Large companies particularly value robust algorithms because their infrastructure depends on efficiency to remain profitable on a massive scale<ref>Rastogi, Rajeev, and Kyuseok Shim (1999). "Scalable algorithms for mining large databases." ''Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining''. ACM.</ref>.<br />
<br />
Processes like cooking a meal or reading a manual to assemble a new piece of furniture are examples of algorithms in everyday life<ref>T.C. (August 29, 2017). [https://www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms "What Are Algorithms?"] ''The Economist''. Retrieved April 28, 2019.</ref>. Algorithms are grounded in logic. The increase in their logical complexity via advancements in technology and human effort have provided the foundations for technological concepts such as artificial intelligence and machine learning<ref>McClelland, Calum. (December 4, 2017). [https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991 "The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning"]. ''Medium''. Retrieved April 28, 2019.</ref>. The influence of algorithms is pervasive and in computer science so it leads to an increase in ethical concerns in the areas of bias, privacy, and accountability.<br />
<br />
<br />
== History ==<br />
The earliest known algorithms stem back to 1700 BC when the Egyptians created them for quick multiplication and division<ref>Stepanov, A. and Rose, D. (2014).<br />
''From Mathematics to Generic Programming: The First Algorithm''. [http://www.informit.com/articles/article.aspx?p=2264460 "Chapter 2"]. Retrieved April 28, 2019.</ref>. Since then, ancient Babylonians in 300 BC created algorithms to track farming and livestock using square roots. Following this, steady advancement gave birth to fundamental mathematical algorithm families like algebra, shaping the field of mathematics with its all-purpose formulas. The man often accredited as “The Father of Algebra,” Muhammad ibn Mūsa al-Khwarizmī, was also the one who gave English the word “algorithm” around 850 AD, as he wrote a book ''Al-Khwarizmi on the Hindu Art of Reckoning'', which in Latin translates to ''Algoritmi de Numero Indorum''. The English word [https://www.digit.in/features/science-and-technology/the-origin-of-algorithms-30045.html "algorithm"] was adopted from this title.<br />
<br />
A myriad of fundamental algorithms have been developed throughout history, ranging from pure mathematics to important computer science stalwarts, extending from ancient times up through the modern day. The computer revolution led to algorithms that can filter and personalize results based on the user.<br />
<br />
The [https://en.wikipedia.org/wiki/Timeline_of_algorithms Algorithm Timeline] outlines the advancements in the development of the algorithm as well as a number of the well-known algorithms developed from the Medieval Period until modern day.<br />
<br />
=== Computation ===<br />
Another cornerstone for algorithms comes from [https://en.wikipedia.org/wiki/Alan_Turing Alan Turing] and his contributions to cognitive and [https://en.wikipedia.org/wiki/Computer_science computer science]. Turing conceptualized the concept of cognition and designed ways to emulate human cognition with machines. This process turned the human thought process into mathematical algorithms and it led to the development of Turing Machines. It capitalized on these theoretical algorithms to perform unique functions and the development of computers. As their name suggests, computers utilized specific rules or algorithms to compute and it is these machines (or sometimes people)<ref>[crgis.ndc.nasa.gov/historic/Human_Computers "Human Computers"]. ''NASA Cultural Resources''. Retrieved April 28, 2019.</ref> that most often relate to the concept of algorithms that is used today. With the advent of mechanical computers, the computer science field paved the way for algorithms to run the world as they do now by calculating and controlling an immense quantity of facets of daily life. To this day, Turing machines are a main area of study in the theory of computation.<br />
<br />
=== Advancements In Algorithms ===<br />
In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity. Advanced algorithms, such as artificial intelligence is defined as utilizing machine learning capabilities.<ref>Anyoha, Rockwell (August 28, 2017). [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ "The History of Artificial Intelligence"]. ''Science in the News''. Harvard. Retrieved April 28, 2019.</ref> This level of algorithmic improvement provided the foundation for more technological advancement.<br />
[[File:machineLearning.png|500px]]<br />
<br />
The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.<br />
<br />
== Classifications ==<br />
There are many different classifications of algorithms, some are more well-suited for particular families of computational problems than others. In many cases, the algorithm one chooses to make for a given problem will have tradeoffs dealing with time complexity and memory usage.<br />
<br />
==== Recursive Algorithms ====<br />
<br />
A [https://en.wikipedia.org/wiki/Recursion_(computer_science) recursive algorithm] is an algorithm that calls itself with decreasing values in order to reach a pre-defined base case solution. The base case solution determines the values that are sent back up the recursive stack to determine the final outcome of the algorithm. It follows the principle of solving subproblems to solve the larger problem since once the base case solution is reached, the algorithm works upwards to fit the solution into the larger subproblem. The base case must be present, otherwise the recursive function will never stop calling itself, creating an infinite loop. Since recursion involves numerous function calls, it is one of the main sources of stack overflow. With recursive calls, programs have to save more stacks despite a lack of available space. Further, some recursive functions require additional computations even after the recursive call, adding to the consumption speed and memory. 'Tail Recursive' functions are an efficient solution to this, wherein recursive calls happen at the very end of the function, allowing only one stack to be saved throughout the function calls.<br />
<br />
Due to the recurring memory stack frames that are created with each call, recursive algorithms generally require more memory and computation power. However, they are still viewed as simplistic and succinct ways to write elaborate algorithms. <br />
<br />
==== Serial, Parallel or Distributed ====<br />
A [https://en.wikipedia.org/wiki/Sequential_algorithm serial algorithm] is an algorithm in which calculation is done in sequential manners on one core, it follows a defined order in solving a problem. Parallel algorithms utilizes the fact that modern computers have more than one cores, so that computations that are not interdependent could be calculated on separate cores. This is referred to as multi-threaded algorithm where each thread is a series of executing commands and they are inter-weaved to ensure correct output without deadlock. A deadlock occurs when there are interdependence between more than one thread so that none of the threads can continue until one of the other threads continues. Parallel algorithm is important that it allows more than one program to run at the same time by leveraging a computer's available resources otherwise would not have been possible with serial algorithm. Finally, distributed algorithm is similar to parallel algorithm in that it allows multiple programs to run at once with the exception that instead of leveraging multiple cores in a single computer it leverages multiple computers that communicates through a computer network. Similar to how parallel algorithm builds on serial algorithm with the added complexity of synchronizing threads to prevent deadlock and ensure correct outputs, distributed algorithm builds on parallel algorithm with the added complexity of managing communication latency and defining order since it is impossible to synchronize every computer's clock in a distributed system without significant compromises. A distributed algorithm provides extra reliability in that data are stored in more than one location so that one failure would not result in loss of data and by doing computation in multiple computer it can potentially have even faster computational speed.<br />
<br />
==== Deterministic vs Non-Deterministic ====<br />
Deterministic algorithms are those that solve problems with exact precision and ordering so a given input would produce the same output every time. Non-deterministic algorithms either have data races or utilize certain randomization, so the same input could have a myriad of outputs. Non-deterministic algorithms can be represented by flowcharts, programming languages, and machine language programs. However, they vary in that they are capable of using a multiple-valued function whose values are the positive integers less than or equal to itself. In addition, all points of termination are labeled as successes or failures. The terminology "non-deterministic" does not imply randomness, of rather a kind of free will<ref>"<br />
Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref><br />
<br />
==== Exact vs Approximation ====<br />
Some algorithms are implemented to solve for the exact solutions of a problem, whereas some problems are implemented for an approximation or heuristic. Approximation is important in which heuristics could provide an answer that is good enough such that the excessive computational time necessary to find the actual solution is not warranted since one would get minimal gains while expanding a great deal of resources. An example where an approximation is warranted is the traveling salesman's algorithm in which has computational complexity of O(n!), so an heuristic is necessary since some high values of n are not even possible to calculate.<br />
<br />
==== Brute Force or Exhaustive Search ====<br />
A brute force algorithm is the most "naive" approach one can take in attempting to solve a particular problem. A solution is reached by searching through every single possible outcome before arriving at an answer. In terms of complexity or Big-O notation, brute force algorithms typically represent the highest order complexity compared to other potential solutions for a given problem. While brute force algorithms may not be considered the most efficient option for solving computational problems, they do offer reliability as well as a guarantee that a solution to a given problem will eventually be found.<br />
<br />
An example of a brute force algorithm would be trying all combinations of a 4-digit passcode, in order to crack into a target's smartphone.<br />
<br />
==== Divide and Conquer ====<br />
A divide and conquer algorithm divides a problem into smaller sub-problems then conquer each smaller problems before merging them together to solve the original problem. In terms of efficiency and the Big-O notation, the Divide and Conquer fares better than Brute Force but is still relatively inefficient compared to other more complex algorithms. An example of divide and conquer is merge sort wherein a list is split into smaller sorted lists and then merged together to sort the original list.<br />
<br />
Examples of Divide and Conquer algorithms would be the sorting algorithm [https://en.wikipedia.org/wiki/Merge_sort Merge Sort], and the searching algorithm [https://en.wikipedia.org/wiki/Binary_search_algorithm Binary Search]<ref>[https://www.geeksforgeeks.org/divide-and-conquer-algorithm-introduction "Divide and Conquer Algorithms"]. ''Geeks for Geeks''. Retrieved April 28, 2019.</ref>.<br />
<br />
====Dynamic Programming====<br />
[https://.wikipedia.org/wiki/Dynamic_programming Dynamic programming] takes advantage of overlapping subproblems to more efficiently solve a larger computational problem. The algorithm first solves less complex subproblems and stores their solutions in memory. Then more complex problems will find these solutions using some method of lookup to find the solution and implement it in the more complex problem to find its own solution. The method of lookup enables solutions to be computed once and used multiple times. This method reduces the time complexity from exponential to polynomial.<br />
<br />
An example of a common problem that can be solved by Dynamic Programming is the [https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm 0-1 Knapsack Problem].<br />
<br />
==== Backtracking ====<br />
A backtracking algorithm is similar to brute force with the exception that as soon as it reaches a node where a solution could never be reached from said node on, it prunes all the subsequent node and backtracks to the closest node that has the possibility to be right. Pruning in this context means neglecting the failed branch as a potential solution branch in all further searches, reducing the scope of the possible solution set and eventually guiding the program to the right outcome.<br />
<br />
An example of some problems that can be solved by algorithms that take advantage of backtracking is solving [[Wikipedia:Sudoku|Sudoko]], or the [[Wikipedia:Eight_queens_puzzle|N-Queens Problem]].<br />
<br />
====Greedy Algorithm====<br />
An intuitive approach to design algorithms that don’t always yield the optimal solution. This approach required a collection of candidates, or options, in which the algorithm selects in order to satisfy a given predicate. Greedy algorithms can either favor the least element in the collection or the greatest in order to satisfy the predicate<ref>[https://brilliant.org/wiki/greedy-algorithm/ "Greedy Algorithms"]. ''Brilliant Math & Science Wiki''. Retrieved April 28, 2019.</ref>.<br />
<br />
An example of a greedy algorithm may take the form of selecting coins to make change in a transaction. The collection includes the official coins of the U.S. currency (25 cents, 10 cents, 5 cents, and 1 cent) and the predicate would be to make change of 11 cents. The greedy algorithm will select the greatest of our collection to approach 11, but not past it. The algorithm will first select a dime, then a penny, then end when 11 cents has been made.<br />
<br />
== Complexity and Big-O Notation ==<br />
[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]<br />
Measuring the efficiency of an algorithm is standardized by checking how well it grows with more inputs. Computer scientists calculate how much computational time increases with an increasing number of inputs. Since this form of measurement merely intends to see how well an algorithm grows, the constants are left out since with high inputs these constants are negligible anyways. Big-O notation specifically describes the worst-case scenario and measures the time or space the algorithm uses<ref name = 'Big-O'>[ Bell, Rob. [https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/ "A Beginner's Guide to Big O Notation"]. Retrieved April 28, 2019.</ref>. Big-O notation can be broken down into order of growth algorithms such as O(1), O(N), and O(log N), with each notation representing different orders of growth. The later, log algorithms -commonly referred to as logarithms, are bit more complex than the rest, log algorithms take a median from a data set and compare it to a target value, the algorithm continues to halve the data as long as the median is higher or lower than the target value<ref name='Big-O'></ref>. An algorithm with a higher Big-O is less efficient at large scales, for example in general a O(N) algorithm will run slower than a O(1) algorithm, and this difference will be more and more apparent, the larger the number of inputs.<br />
<br />
== Artificial Intelligence Algorithms ==<br />
=== Clustering ===<br />
Clustering is a [https://en.wikipedia.org/wiki/Machine_learning Machine Learning] technique in which, data is segregated into groups called clusters through an algorithm, given a set of data points. These clustering algorithms classify the data based on various criteria, but the fundamental premise is that data points with similarities will belong in the same group, that must be dissimilar to other groups. There are numerous clustering algorithms including K-Means clustering, Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering, EM (Expectation Maximization) Clustering, Agglomerative Hierarchical Clustering. <ref name="Clustering">Seif, George (February 5, 2018). [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 "The 5 Clustering Algorithms Data Scientists Need To Know"]. Retrieved April 28, 2019.</ref><br />
<br />
==== K-Means Clustering ====<br />
K-means clustering is the most widely known and used algorithm out of all the clustering algorithms. It involves pre-determining a target number - ''k'', which represents the number of centroids needed in the dataset. A centroid refers to the predicted center of the cluster. It then identifies the data points nearest to the center to form each cluster, while keeping ''k'' as small as possible. <ref>Garbade, Michael J. (September 12, 2018). [https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 "Understanding K-Means Clustering in Machine Learning"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> K-Means clustering is considered to be a fast algorithm, due to the minimal computations it requires. It has a Big-O complexity of O(''n''). <ref name="Clustering"></ref> <br />
<br />
==== Mean-Shift Clustering ====<br />
Mean-Shift Clustering, also known as Mode-Seeking, is an algorithm where datapoints are grouped into clusters, through the process of iteratively shifting all the points towards their mode. The mode of a dataset is defined as the most occurring value in that particular dataset, or in graphical terms, the point where the density of data points is the highest. Therefore, the algorithm moves, or "shifts" each point closer to its closest centroid, the direction of which is determined by the density of the nearby points. Therefore, each iteration of the program moves each point closer to where all the other points are, eventually forming a cluster center. The key difference between Mean-Shift and K-Means clustering is that K-Means requires the number ''k'' to be set beforehand, whereas the Mean-Shift algorithm creates clusters on the go without necessarily determining how many will be formed. <ref>[http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/ "Meanshift Algorithm for the Rest of Us (Python)"], May 14, 2016. Retrieved April 28, 2019.</ref> Usually, the Big-O complexity of such an algorithm is O(''Tn^2''), where ''T'' refers to the number of iterations in the algorithm. <ref>Thirumuruganathan, Saravanan (April 1, 2010). [https://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/ "Introduction To Mean Shift Algorithm"]. Retrieved April 28, 2019.</ref><br />
<br />
==== DBSCAN Clustering ====<br />
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that groups together nearby datapoints based on a measure of distance, often [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] and a minimal number of points. The algorithm also differentiates outliers if they are in low density areas. The algorithm requires two parameters - ''eps'' and ''minPoints''. The decision on what to set these parameters as depends from dataset to dataset, and requires a fundamental understanding of the context of the dataset being used. The ''eps'' should be picked based on the dataset distance and while generally small ''eps'' values are desirable, if the set value is too small there is a danger that a portion of the data will go unclustered. Conversely, if the value set is too large, too many of the points might get grouped into the same cluster. The minPoints parameter is usually derived from the parameter ''D'', following that minPoints ≥ ''D + 1'' where ''D'' measures the number of dimensions in the data. <ref>Salton do Prado, Kelvin (April 1, 2017). [https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80 "How DBSCAN works and why should we use it?"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> The average run-time complexity of a DBSCAN algorithm is O(''n log n'') whereas it's worst-case complexity can be O(''n^2'').<br />
<br />
==== EM Clustering ====<br />
Expectation Maximization or EM Clustering is similar to the K-Means clustering technique. The EM Clustering method takes forward the basic principles of K-Means Clustering in two primary ways:<br />
1) The EM Clustering method aims to calculate what datapoints belong in what cluster through one or more probability distributions, instead of trying to simply calculate and maximize the difference in mean points.<br />
2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.<br />
<br />
Essentially, the EM clustering method approximates the distribution of each point based on different probability distributions and at the end of it, each observation has a certain level of probability of belonging to a particular cluster. Ultimately, the resulting clusters are analyzed based on which clusters have the highest classification probabilities. <ref>[https://docs.rapidminer.com/latest/studio/operators/modeling/segmentation/expectation_maximization_clustering.html "Expectation Maximization Clustering"] ''RapidMiner Documentation''. Retrieved April 28, 2019.</ref><br />
<br />
==== Agglomerative Hierarchical Clustering ====<br />
Also known as AGNES (Agglomerative Nesting), the Agglomerative Hierarchical Clustering Technique also creates clusters based on similarity. To start, this method takes each object and treats it as if it were a cluster. It then merges each of these clusters in pairs, until one huge cluster consisting of all the individual clusters has been formed. The result is represented in the form of a tree, which is called a ''dendrogram''. The manner in which the algorithm works is called the "bottom-up" technique. Each data entry is considered an individual element or a leaf node. At each following stage, the element is joined with its closest or most-similar element to form a bigger element or parent node. The process is repeated over and over until the root node is formed, with all of the subsequent nodes under it.<br />
<br />
The opposite of this technique is through the "top-down" method, which is implemented in an algorithm called "Divisive Clustering". This method starts at the root node, and at each iteration nodes are split or "divided" into two separate nodes, based off the ranking of dissimilarity within the clusters. This is done until every node has been divided, leaving individual clusters or leaf nodes. <ref>[https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/ "HIERARCHICAL CLUSTERING IN R: THE ESSENTIALS/Agglomerative Hierarchical Clustering"]. ''DataNovia''. Retrieved April 28, 2019.</ref><br />
<br />
==== Deep Learning and Neural Networks ====<br />
Neural networks are a collection of algorithms that utilize many of the concepts mentioned above while taking their capabilities a step further through deep learning. On a high level, the purpose of a neural network is to interpret raw input data through a machine perception and return patterns in the data, through techniques above such as K-means clustering or Random Forests. To do so, a neural network requires datasets to train on and thus models its interpretations on the training sets in a machine learning process. Where neural networks differ is its ability to be “stacked” to engage in deep learning. Each process is held in nodes that can be likened to neurons in a human brain. When data is encountered, many separate computations occur that can be weighted to produce the desired output. How many “layers”, or the depth, of a neural network increases its capabilities and complexity multiplicatively. <ref> A Beginner's Guide to Neural Networks and Deep Learning. (n.d.). Retrieved April 27, 2019, from https://skymind.ai/wiki/neural-network </ref><br />
<br />
== Ethical Dilemmas ==<br />
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.<br />
<br />
=== Bias ===<br />
Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. <br />
<br />
====Joy Buolamwini and Facial Recognition====<br />
Joy Buolamwini, a graduate computer science student at MIT, experienced a case of this. The facial recognition software she was working on failed to detect her face, as she had a skin tone that had not been accounted for in the facial recognition algorithm. This is because the software had used machine learning with a dataset that was not diverse enough, and as a result, the algorithm failed to recognize her.<ref>Buolamwini, Joy. [www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/ "How I'm Fighting Bias in Algorithms"]. ''MIT Media Lab''. Retrieved April 28, 2019.</ref> Safiya Noble discusses instances of algorithmic search engines reinforcing racism in her book, "Algorithms of Oppression".<ref>Noble, Safiya. ''Algorithms of Oppression''.</ref> Bias like this occurs in countless algorithms, be it through insufficient machine learning data sets, or the algorithm developers own fault, among other reasons, and it has the potential to cause legitimate problems even outside the realm of ethics.<br />
<br />
====Bias in Criminalization====<br />
[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing COMPAS] is algorithm written to determine whether a criminal is likely to re-offend using information like age, gender, and previously committed crimes. Tests have found it to be more likely to incorrectly evaluate black people than white people because it has learned on historical criminal data, which has been influenced by our biased policing practices.<ref>Larson, Mattu; Kirchner, Angwin (May 23, 2016). [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm "How We Analyzed the COMPAS Recidivism Algorithm"]. ''Propublica''. Retrieved April 28, 2019.</ref> <br />
<br />
Jerry Kaplan is a research affiliate at Stanford University’s Center on Democracy, Development and the Rule of Law at the Freeman Spogli Institute for International Studies, where he teaches “Social and Economic Impact of Artificial Intelligence.” According to Kaplan, algorithmic bias can even influence whether or not a person is sent to jail. A 2016 study conducted by ProPublica indicated that software designed to predict the likelihood an arrestee will re-offend incorrectly flagged black defendants twice as frequently as white defendants in a decision-support system widely used by judges. Ideally, predictive systems should be wholly impartial and therefore be agnostic to skin color. However, surprisingly, the program cannot give black and white defendants who are otherwise identical the same risk score, and simultaneously match the actual recidivism rates for these two groups. This is because black defendants are re-arrested at higher rates that their white counterparts (52% versus 39%), at least in part due to racial profiling, inequities in enforcement, and harsher treatment of black people within the justice system. <ref>Kaplan, J. (December 17, 2018). [https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/ "Why your AI might be racist"]. ''Washington Post''. Retrieved April 28, 2019.</ref><br />
<br />
==== Job Applicants ====<br />
Many companies employ complex algorithms in order to review and sift the thousands of resumes they will receive each year. Sometimes these algorithms display a bias which can result in people with a specific racial background or gender being recommended over others. An example of this was an Amazon AI algorithm that preferred men over women in recommending people for an interview. The algorithm employed machine learning techniques and over time taught itself to prefer men over women due to a variety of factors <ref>Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women.” Reuters, Thomson Reuters, 9 Oct. 2018, af.reuters.com/.</ref>. A major problem facing machine learning algorithms is the unpredictability in their models and what they will begin to teach themselves. It was apparent Amazon engineers did not intend for their algorithm to be bias towards men but an error resulted in this happening. Additionally, this was not a quick fix as the algorithm had been in place for years and began to pick up this bias and after analysis after a period time, it was discovered.<br />
<br />
=== Privacy And Data Gathering ===<br />
The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref>Turilli, Matteo, and Luciano Floridi (2009). "The Ethics of Information Transparency." ''Ethics and Information Technology''. '''11'''(2): 105-112. doi:10.1007/s10676-009-9187-9.</ref> is an import point regarding these issues. In popular social media algorithms, user information is often probed without the knowledge of the individual, and this can lead to problems. It is often not transparent enough how these algorithms receive user data, resulting in often incorrect information which can affect both how a person is treated within social media, as well as how outside agents could view these individuals given false data. Algorithms can also often infringe on a user’s feelings of privacy, as data can be collected that a person would prefer to be private. Data brokers are in the business of collecting peoples in formation and selling it to anyone for a profit, like data brokers companies often have their own collection of data. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref>Griffin, Andrew (October 4, 2017) [http://www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html "Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth."] ''The Independent''. Retrieved April 28, 2019.</ref> The information leaked contained data relating to usernames, passwords, as well as dates of birth. Privacy and data gathering are common ethical dilemmas relating to algorithms and are often not considered thoroughly enough by algorithm’s users.<br />
<br />
===The Filter Bubble===<br />
[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]<br />
Algorithms can be used to filter results in order to prioritize items that the user might be interested in. On some platforms, like Amazon, people can find this filtering useful because of the useful shopping recommendations the algorithm can provide. However, in other scenarios, this algorithmic filtering can become a problem. For example, Facebook has an algorithm that re-orders the user's news feed. For a period of time, the technology company prioritized sponsored posts in their algorithm. This often prioritized news articles, but there was no certainty on whether these articles came from a reliable source, simply the fact that they were sponsored. Facebook also uses its technology to gather information about its users, like which political party they belong to. This combined with prioritizing news can create a Facebook feed filled with only one party's perspective. This phenomenon is called the filter bubble, which essentially creates a platform centered completely around its user's interests. <br />
<br />
Many, like Eli Pariser, have questioned the ethical implications of the filter bubble. Pariser believes that filter bubbles are a problem because they prevent users from seeing perspectives that might challenge their own. Even worse, Pariser emphasizes that this filter bubble is invisible, meaning that the people in it do not realize that they are in it. <ref>Pariser, Eli. (2012). ''The Filter Bubble''. Penguin Books.</ref> This creates a huge lack of awareness in the world, allowing people to stand by often uninformed opinions and creating separation, instead of collaboration, with users who have different beliefs. Because of the issues Pariser outlined, Facebook decided to change their algorithm in order to prioritize posts from friends and family, in hopes of eliminating the effects of the potential filter bubble.<br />
====Filter Bubble in Politics====<br />
Another issue that these Filter Bubbles create are echo chambers; Facebook, in particular, filters out [political] content that one might disagree with, or simply not enjoy <ref>Knight, Megan (November 30, 2018). [http://theconversation.com/explainer-how-facebook-has-become-the-worlds-largest-echo-chamber-91024 "Explainer: How Facebook Has Become the World's Largest Echo Chamber"]. ''The Conversation''. Retrieved April 28, 2019.</ref>. The more a user "likes" a particular type of content, similar content will continue to appear, and perhaps content that is even more extreme. This was clearly seen in the 2016 election when without realizing it, voters developed tunnel vision. Rarely did their Facebook comfort zones expose them to opposing views, and as a result they eventually became victims to their own biases and the biases embedded within the algorithms.<ref>El-Bermawy, Mostafa M. (June 3, 2017). [https://www.wired.com/2016/11/filter-bubble-destroying-democracy/ "Your Filter Bubble Is Destroying Democracy"]. ''Wired''. Retrieved April 28, 2019.</ref> Later studies produced visualizations to show how insular the country was at the time of the election on social media and the large divide between the two echo chambers with almost no ties to each other.<ref>MIS2: Misinformation and Misbehavior Mining on the Web - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 [accessed 23 Apr, 2019]</ref> <br />
<br />
[[File:Socal-med-polarizing.png|thumbnail|right|From [https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 Research Gate]: An example of algorithmic echo chambers that contribute to the polarization of political beliefs]]<br />
<br />
===Corrupt Personalization===<br />
Algorithms have the potential to become dangerous, with their most serious repercussions being the threat to democracy that is extensive personalization. Algorithms such as Facebook's are corrupt in the practice of "like recycling" that they partake in. In Christian Sandvig's article title ''Corrupt Personalization,'' he notes that Facebook has defined a "like" in two ways that the users do not realize. The first is that "anyone who clicks on a "like" button is considered to have "liked" all future content from that source," and the second is that "anyone who "likes" a comment on a shared link is considered to "like" wherever that link points to" <ref>Sandvig, Christian. “Corrupt Personalization.” Social Media Collective, 27 June 2014, socialmediacollective.org/2014/06/26/corrupt-personalization/</ref>. As a result, posts that you "like" wind up becoming ads on your friends' pages claiming that you like a certain item or thing. You are not able to see these posts and, because they do not appear on your news feed, you do not have the power to delete them. This becomes a threat to one's autonomy, for even if they wanted to delete this post, they can not. Furthermore, everyone is entitled to the ability to manage the public presentation of their own self-identity, and in this corrupt personalization, Facebook is giving users new aspects of their identity that may or may not be accurate. <br />
<br />
=== Agency And Accountability ===<br />
Algorithms make "decisions" based on the steps they were designed to follow and the input they received. This can often lead to algorithms as autonomous agents<ref>“Autonomous Agent.” Autonomous Agent - an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/computer-science/autonomous-agent.</ref>, taking decision making responsibilities out of the hands of real people. Useful in terms of efficiency, these autonomous agents are capable of making decisions in a greater frequency than humans. Efficiency is what materializes the baseline for algorithm use in the first place.<br />
<br />
From an ethical standpoint, this type of agency raises many complications, specifically regarding accountability. It is no secret that many aspects of life are run by algorithms. Even events like applying to jobs are often drastically effected by these processes. Information like age, race, status, along with other qualifications, are all fed to algorithms, which then take agency and decide who moves further along in the hiring process and who is left behind. Disregarding inherent biases in this specific scenario, this process still serves to reduce the input of real humans and decrease the number of decisions that they have to make, and what is left over is the fact that autonomous agents are making systematic decisions that have extraordinary impact on people's lives. While the results of the previous example may only culminate to the occasional disregard of a qualified applicant or resentful feelings, this same principle can be much more influential.<br />
<br />
====The Trolley Problem in Practice====<br />
Consider autonomous vehicles, or self-driving cars, for instance. These are highly advanced algorithms that are programmed to make split second decisions with the greatest possible accuracy. In the case of the well-known "Trolley Problem"<ref>Roff, Heather M. “The Folly of Trolleys: Ethical Challenges and Autonomous Vehicles.” Brookings, Brookings, 17 Dec. 2018, www.brookings.edu/research/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/.</ref>, these agents are forced to make a decision jeopardizing one party or another. This decision can easily conclude in the injury or even death of individuals, all at the discretion of a mere program. <br />
<br />
The issue of accountability is then raised, in a situation such as this, because in the eyes of the law, society, and ethical observers, there must be someone held responsible. Attempting to prosecute a program would not be feasible in a legal situation, due to not being able to have a physical representation of the program, like you would a person. However, there are those such as Frances Grodzinsky and Kirsten Martin <ref>Martin, Kirsten. “Ethical Implications and Accountability of Algorithms.” SpringerLink, Springer Netherlands, 7 June 2018, link.springer.com/article/10.1007/s10551-018-3921-3.</ref>, who believe that the designers of an artificial agent, should be responsible for the actions of the program. <ref name="Grodzinsky"> "The ethics of designing artificial agents" by Frances S. Grodzinsky et al, Springer, 2008.</ref> Others contend this point by saying that the blame should be attributed to the users or persons directly involved in the situation. <br />
<br />
These complications will continue to arise, especially as algorithms continue to make autonomous decisions at grander scales and rates. Determining responsibility for the decisions these agents make will continue to be a vexing process, and will no doubt shape in some form many of the advanced algorithms that will be developed in the coming years.<br />
<br />
=== Intentions and Consequences ===<br />
The ethical consequences that are common in algorithm implementations can be either deliberate or unintentional. Instances where an algorithm's intent and outcome differs is noted below. <br />
<br />
==== YouTube Radicalization ====<br />
Scholar and technosociologist Zeynep Tufekci has claimed that "[[YouTube]] may be one of the most powerful radicalizing instruments of the 21st century."<ref name="YouTube Radicalization">[https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html] Tufekci, Zeynep. “YouTube, the Great Radicalizer.” The New York Times, The New York Times, 10 Mar. 2018, www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html.</ref> As YouTube algorithms aim maximize the amount of time that viewers spend watching, it inevitably discovered that the best way to do this was to show videos that slowly "up the stakes" of the subject being watched - from jogging to ultramarathons, from vegetarianism to veganism, from Trump speeches to white supremacist rants.<ref name="YouTube Radicalization" /> Thus, while the intention of Youtube is to keep viewers watching (and bring in advertisement money), they have unintentionally created a site that shows viewers more and more extreme content - contributing to radicalization. Such activity circles back to and produce filters and echo chambers.<br />
<br />
==== Facebook Advertising ====<br />
By taking a closer look at Facebook's algorithm that serves up ads to its users, gender and racial bias is obviously prominent. Using demographic and racial background as factors, Facebook's decides which ads are served up to its users. A team from Northeastern tested to see the algorithm bias in action and by running identical ads with slight tweaks to budget, images, and headings, the ads reached vastly different audiences. Results such as minorities receiving a higher percentage of low-cost housing ads, and women receiving more ads for secretary and nursing jobs <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Although the intent of Facebook may be to reach people that these ads are intended for, the companies that are signing up to advertise with Facebook have stated they did not anticipate this type of filtering when paying for their services. Additionally, although Facebook may believe it is win-win for everyone since advertisers will be getting more interactions with certain audiences targeted, and people will be happy to see ads more relatable to them shown, it is incredibly discriminatory to target people based on factors that are uncontrollable. Facebook needs to adjust its targeted advertising practices by removing racial and gender factors in their algorithms in order to prevent perpetuating stereotypes and placing people in certain boxes by race and gender. This type of algorithm goes against many ethical principles and is important that powerful technology companies are not setting poor examples for others.<br />
<br />
==See also==<br />
{{resource|<br />
*[[Bias in Information]]<br />
*[[Artificial Agents]]<br />
*[[Value Sensitive Design]]<br />
*[[Artificial Intelligence and Technology]]<br />
}}<br />
<br />
== References ==<br />
<references/><br />
[[Category:2019New]]<br />
[[Category:Concepts]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Algorithms&diff=91442Algorithms2021-02-11T17:54:46Z<p>WikiSysop: </p>
<hr />
<div>[[File:Algorithm.png|200px|thumb|right]]<br />
{{Nav-Bar|Topics##}}<br><br />
An '''Algorithm''' is defined as a set of precise steps and distinct states used to express the detailed structure of a program or the order of events that occurred in a system <ref>Cormen, Thomas H. et al. (2009). ''Introduction to Algorithms;'. MIT Press.</ref>.Algorithms are involved in many aspects of daily life and in complex computer science concepts. They often use repetition of operations to allow people and machines to execute tasks more efficiently by executing tasks faster and using fewer resources such as memory. On a basic level, an algorithm is a system working through different iterations of a process<ref>Lim, Brian (December 7, 2016). [e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/ "A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life)"] ''e27''.</ref>. They can help turn systematic and tedious tasks into fast, automated processes. Large companies particularly value robust algorithms because their infrastructure depends on efficiency to remain profitable on a massive scale<ref>Rastogi, Rajeev, and Kyuseok Shim (1999). "Scalable algorithms for mining large databases." ''Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining''. ACM.</ref>.<br />
<br />
'''Processes like cooking a meal or reading a manual to assemble a new piece of furniture are examples of algorithms in everyday life'''<ref>T.C. (August 29, 2017). [https://www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms "What Are Algorithms?"] ''The Economist''. Retrieved April 28, 2019.</ref>. Algorithms are grounded in logic. The increase in their logical complexity via advancements in technology and human effort have provided the foundations for technological concepts such as artificial intelligence and machine learning<ref>McClelland, Calum. (December 4, 2017). [https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991 "The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning"]. ''Medium''. Retrieved April 28, 2019.</ref>. The influence of algorithms is pervasive and in computer science so it leads to an increase in ethical concerns in the areas of bias, privacy, and accountability.<br />
<br />
<br />
== History ==<br />
The earliest known algorithms stem back to 1700 BC when the Egyptians created them for quick multiplication and division<ref>Stepanov, A. and Rose, D. (2014).<br />
''From Mathematics to Generic Programming: The First Algorithm''. [http://www.informit.com/articles/article.aspx?p=2264460 "Chapter 2"]. Retrieved April 28, 2019.</ref>. Since then, ancient Babylonians in 300 BC created algorithms to track farming and livestock using square roots. Following this, steady advancement gave birth to fundamental mathematical algorithm families like algebra, shaping the field of mathematics with its all-purpose formulas. The man often accredited as “The Father of Algebra,” Muhammad ibn Mūsa al-Khwarizmī, was also the one who gave English the word “algorithm” around 850 AD, as he wrote a book ''Al-Khwarizmi on the Hindu Art of Reckoning'', which in Latin translates to ''Algoritmi de Numero Indorum''. The English word [https://www.digit.in/features/science-and-technology/the-origin-of-algorithms-30045.html "algorithm"] was adopted from this title.<br />
<br />
A myriad of fundamental algorithms have been developed throughout history, ranging from pure mathematics to important computer science stalwarts, extending from ancient times up through the modern day. The computer revolution led to algorithms that can filter and personalize results based on the user.<br />
<br />
The [https://en.wikipedia.org/wiki/Timeline_of_algorithms Algorithm Timeline] outlines the advancements in the development of the algorithm as well as a number of the well-known algorithms developed from the Medieval Period until modern day.<br />
<br />
=== Computation ===<br />
Another cornerstone for algorithms comes from [https://en.wikipedia.org/wiki/Alan_Turing Alan Turing] and his contributions to cognitive and [https://en.wikipedia.org/wiki/Computer_science computer science]. Turing conceptualized the concept of cognition and designed ways to emulate human cognition with machines. This process turned the human thought process into mathematical algorithms and it led to the development of Turing Machines. It capitalized on these theoretical algorithms to perform unique functions and the development of computers. As their name suggests, computers utilized specific rules or algorithms to compute and it is these machines (or sometimes people)<ref>[crgis.ndc.nasa.gov/historic/Human_Computers "Human Computers"]. ''NASA Cultural Resources''. Retrieved April 28, 2019.</ref> that most often relate to the concept of algorithms that is used today. With the advent of mechanical computers, the computer science field paved the way for algorithms to run the world as they do now by calculating and controlling an immense quantity of facets of daily life. To this day, Turing machines are a main area of study in the theory of computation.<br />
<br />
=== Advancements In Algorithms ===<br />
In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity. Advanced algorithms, such as artificial intelligence is defined as utilizing machine learning capabilities.<ref>Anyoha, Rockwell (August 28, 2017). [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ "The History of Artificial Intelligence"]. ''Science in the News''. Harvard. Retrieved April 28, 2019.</ref> This level of algorithmic improvement provided the foundation for more technological advancement.<br />
[[File:machineLearning.png|500px]]<br />
<br />
The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.<br />
<br />
== Classifications ==<br />
There are many different classifications of algorithms, some are more well-suited for particular families of computational problems than others. In many cases, the algorithm one chooses to make for a given problem will have tradeoffs dealing with time complexity and memory usage.<br />
<br />
==== Recursive Algorithms ====<br />
<br />
A [https://en.wikipedia.org/wiki/Recursion_(computer_science) recursive algorithm] is an algorithm that calls itself with decreasing values in order to reach a pre-defined base case solution. The base case solution determines the values that are sent back up the recursive stack to determine the final outcome of the algorithm. It follows the principle of solving subproblems to solve the larger problem since once the base case solution is reached, the algorithm works upwards to fit the solution into the larger subproblem. The base case must be present, otherwise the recursive function will never stop calling itself, creating an infinite loop. Since recursion involves numerous function calls, it is one of the main sources of stack overflow. With recursive calls, programs have to save more stacks despite a lack of available space. Further, some recursive functions require additional computations even after the recursive call, adding to the consumption speed and memory. 'Tail Recursive' functions are an efficient solution to this, wherein recursive calls happen at the very end of the function, allowing only one stack to be saved throughout the function calls.<br />
<br />
Due to the recurring memory stack frames that are created with each call, recursive algorithms generally require more memory and computation power. However, they are still viewed as simplistic and succinct ways to write elaborate algorithms. <br />
<br />
==== Serial, Parallel or Distributed ====<br />
A [https://en.wikipedia.org/wiki/Sequential_algorithm serial algorithm] is an algorithm in which calculation is done in sequential manners on one core, it follows a defined order in solving a problem. Parallel algorithms utilizes the fact that modern computers have more than one cores, so that computations that are not interdependent could be calculated on separate cores. This is referred to as multi-threaded algorithm where each thread is a series of executing commands and they are inter-weaved to ensure correct output without deadlock. A deadlock occurs when there are interdependence between more than one thread so that none of the threads can continue until one of the other threads continues. Parallel algorithm is important that it allows more than one program to run at the same time by leveraging a computer's available resources otherwise would not have been possible with serial algorithm. Finally, distributed algorithm is similar to parallel algorithm in that it allows multiple programs to run at once with the exception that instead of leveraging multiple cores in a single computer it leverages multiple computers that communicates through a computer network. Similar to how parallel algorithm builds on serial algorithm with the added complexity of synchronizing threads to prevent deadlock and ensure correct outputs, distributed algorithm builds on parallel algorithm with the added complexity of managing communication latency and defining order since it is impossible to synchronize every computer's clock in a distributed system without significant compromises. A distributed algorithm provides extra reliability in that data are stored in more than one location so that one failure would not result in loss of data and by doing computation in multiple computer it can potentially have even faster computational speed.<br />
<br />
==== Deterministic vs Non-Deterministic ====<br />
Deterministic algorithms are those that solve problems with exact precision and ordering so a given input would produce the same output every time. Non-deterministic algorithms either have data races or utilize certain randomization, so the same input could have a myriad of outputs. Non-deterministic algorithms can be represented by flowcharts, programming languages, and machine language programs. However, they vary in that they are capable of using a multiple-valued function whose values are the positive integers less than or equal to itself. In addition, all points of termination are labeled as successes or failures. The terminology "non-deterministic" does not imply randomness, of rather a kind of free will<ref>"<br />
Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref><br />
<br />
==== Exact vs Approximation ====<br />
Some algorithms are implemented to solve for the exact solutions of a problem, whereas some problems are implemented for an approximation or heuristic. Approximation is important in which heuristics could provide an answer that is good enough such that the excessive computational time necessary to find the actual solution is not warranted since one would get minimal gains while expanding a great deal of resources. An example where an approximation is warranted is the traveling salesman's algorithm in which has computational complexity of O(n!), so an heuristic is necessary since some high values of n are not even possible to calculate.<br />
<br />
==== Brute Force or Exhaustive Search ====<br />
A brute force algorithm is the most "naive" approach one can take in attempting to solve a particular problem. A solution is reached by searching through every single possible outcome before arriving at an answer. In terms of complexity or Big-O notation, brute force algorithms typically represent the highest order complexity compared to other potential solutions for a given problem. While brute force algorithms may not be considered the most efficient option for solving computational problems, they do offer reliability as well as a guarantee that a solution to a given problem will eventually be found.<br />
<br />
An example of a brute force algorithm would be trying all combinations of a 4-digit passcode, in order to crack into a target's smartphone.<br />
<br />
==== Divide and Conquer ====<br />
A divide and conquer algorithm divides a problem into smaller sub-problems then conquer each smaller problems before merging them together to solve the original problem. In terms of efficiency and the Big-O notation, the Divide and Conquer fares better than Brute Force but is still relatively inefficient compared to other more complex algorithms. An example of divide and conquer is merge sort wherein a list is split into smaller sorted lists and then merged together to sort the original list.<br />
<br />
Examples of Divide and Conquer algorithms would be the sorting algorithm [https://en.wikipedia.org/wiki/Merge_sort Merge Sort], and the searching algorithm [https://en.wikipedia.org/wiki/Binary_search_algorithm Binary Search]<ref>[https://www.geeksforgeeks.org/divide-and-conquer-algorithm-introduction "Divide and Conquer Algorithms"]. ''Geeks for Geeks''. Retrieved April 28, 2019.</ref>.<br />
<br />
====Dynamic Programming====<br />
[https://.wikipedia.org/wiki/Dynamic_programming Dynamic programming] takes advantage of overlapping subproblems to more efficiently solve a larger computational problem. The algorithm first solves less complex subproblems and stores their solutions in memory. Then more complex problems will find these solutions using some method of lookup to find the solution and implement it in the more complex problem to find its own solution. The method of lookup enables solutions to be computed once and used multiple times. This method reduces the time complexity from exponential to polynomial.<br />
<br />
An example of a common problem that can be solved by Dynamic Programming is the [https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm 0-1 Knapsack Problem].<br />
<br />
==== Backtracking ====<br />
A backtracking algorithm is similar to brute force with the exception that as soon as it reaches a node where a solution could never be reached from said node on, it prunes all the subsequent node and backtracks to the closest node that has the possibility to be right. Pruning in this context means neglecting the failed branch as a potential solution branch in all further searches, reducing the scope of the possible solution set and eventually guiding the program to the right outcome.<br />
<br />
An example of some problems that can be solved by algorithms that take advantage of backtracking is solving [[Wikipedia:Sudoku|Sudoko]], or the [[Wikipedia:Eight_queens_puzzle|N-Queens Problem]].<br />
<br />
====Greedy Algorithm====<br />
An intuitive approach to design algorithms that don’t always yield the optimal solution. This approach required a collection of candidates, or options, in which the algorithm selects in order to satisfy a given predicate. Greedy algorithms can either favor the least element in the collection or the greatest in order to satisfy the predicate<ref>[https://brilliant.org/wiki/greedy-algorithm/ "Greedy Algorithms"]. ''Brilliant Math & Science Wiki''. Retrieved April 28, 2019.</ref>.<br />
<br />
An example of a greedy algorithm may take the form of selecting coins to make change in a transaction. The collection includes the official coins of the U.S. currency (25 cents, 10 cents, 5 cents, and 1 cent) and the predicate would be to make change of 11 cents. The greedy algorithm will select the greatest of our collection to approach 11, but not past it. The algorithm will first select a dime, then a penny, then end when 11 cents has been made.<br />
<br />
== Complexity and Big-O Notation ==<br />
[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]<br />
Measuring the efficiency of an algorithm is standardized by checking how well it grows with more inputs. Computer scientists calculate how much computational time increases with an increasing number of inputs. Since this form of measurement merely intends to see how well an algorithm grows, the constants are left out since with high inputs these constants are negligible anyways. Big-O notation specifically describes the worst-case scenario and measures the time or space the algorithm uses<ref name = 'Big-O'>[ Bell, Rob. [https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/ "A Beginner's Guide to Big O Notation"]. Retrieved April 28, 2019.</ref>. Big-O notation can be broken down into order of growth algorithms such as O(1), O(N), and O(log N), with each notation representing different orders of growth. The later, log algorithms -commonly referred to as logarithms, are bit more complex than the rest, log algorithms take a median from a data set and compare it to a target value, the algorithm continues to halve the data as long as the median is higher or lower than the target value<ref name='Big-O'></ref>. An algorithm with a higher Big-O is less efficient at large scales, for example in general a O(N) algorithm will run slower than a O(1) algorithm, and this difference will be more and more apparent, the larger the number of inputs.<br />
<br />
== Artificial Intelligence Algorithms ==<br />
=== Clustering ===<br />
Clustering is a [https://en.wikipedia.org/wiki/Machine_learning Machine Learning] technique in which, data is segregated into groups called clusters through an algorithm, given a set of data points. These clustering algorithms classify the data based on various criteria, but the fundamental premise is that data points with similarities will belong in the same group, that must be dissimilar to other groups. There are numerous clustering algorithms including K-Means clustering, Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering, EM (Expectation Maximization) Clustering, Agglomerative Hierarchical Clustering. <ref name="Clustering">Seif, George (February 5, 2018). [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 "The 5 Clustering Algorithms Data Scientists Need To Know"]. Retrieved April 28, 2019.</ref><br />
<br />
==== K-Means Clustering ====<br />
K-means clustering is the most widely known and used algorithm out of all the clustering algorithms. It involves pre-determining a target number - ''k'', which represents the number of centroids needed in the dataset. A centroid refers to the predicted center of the cluster. It then identifies the data points nearest to the center to form each cluster, while keeping ''k'' as small as possible. <ref>Garbade, Michael J. (September 12, 2018). [https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 "Understanding K-Means Clustering in Machine Learning"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> K-Means clustering is considered to be a fast algorithm, due to the minimal computations it requires. It has a Big-O complexity of O(''n''). <ref name="Clustering"></ref> <br />
<br />
==== Mean-Shift Clustering ====<br />
Mean-Shift Clustering, also known as Mode-Seeking, is an algorithm where datapoints are grouped into clusters, through the process of iteratively shifting all the points towards their mode. The mode of a dataset is defined as the most occurring value in that particular dataset, or in graphical terms, the point where the density of data points is the highest. Therefore, the algorithm moves, or "shifts" each point closer to its closest centroid, the direction of which is determined by the density of the nearby points. Therefore, each iteration of the program moves each point closer to where all the other points are, eventually forming a cluster center. The key difference between Mean-Shift and K-Means clustering is that K-Means requires the number ''k'' to be set beforehand, whereas the Mean-Shift algorithm creates clusters on the go without necessarily determining how many will be formed. <ref>[http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/ "Meanshift Algorithm for the Rest of Us (Python)"], May 14, 2016. Retrieved April 28, 2019.</ref> Usually, the Big-O complexity of such an algorithm is O(''Tn^2''), where ''T'' refers to the number of iterations in the algorithm. <ref>Thirumuruganathan, Saravanan (April 1, 2010). [https://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/ "Introduction To Mean Shift Algorithm"]. Retrieved April 28, 2019.</ref><br />
<br />
==== DBSCAN Clustering ====<br />
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that groups together nearby datapoints based on a measure of distance, often [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] and a minimal number of points. The algorithm also differentiates outliers if they are in low density areas. The algorithm requires two parameters - ''eps'' and ''minPoints''. The decision on what to set these parameters as depends from dataset to dataset, and requires a fundamental understanding of the context of the dataset being used. The ''eps'' should be picked based on the dataset distance and while generally small ''eps'' values are desirable, if the set value is too small there is a danger that a portion of the data will go unclustered. Conversely, if the value set is too large, too many of the points might get grouped into the same cluster. The minPoints parameter is usually derived from the parameter ''D'', following that minPoints ≥ ''D + 1'' where ''D'' measures the number of dimensions in the data. <ref>Salton do Prado, Kelvin (April 1, 2017). [https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80 "How DBSCAN works and why should we use it?"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> The average run-time complexity of a DBSCAN algorithm is O(''n log n'') whereas it's worst-case complexity can be O(''n^2'').<br />
<br />
==== EM Clustering ====<br />
Expectation Maximization or EM Clustering is similar to the K-Means clustering technique. The EM Clustering method takes forward the basic principles of K-Means Clustering in two primary ways:<br />
1) The EM Clustering method aims to calculate what datapoints belong in what cluster through one or more probability distributions, instead of trying to simply calculate and maximize the difference in mean points.<br />
2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.<br />
<br />
Essentially, the EM clustering method approximates the distribution of each point based on different probability distributions and at the end of it, each observation has a certain level of probability of belonging to a particular cluster. Ultimately, the resulting clusters are analyzed based on which clusters have the highest classification probabilities. <ref>[https://docs.rapidminer.com/latest/studio/operators/modeling/segmentation/expectation_maximization_clustering.html "Expectation Maximization Clustering"] ''RapidMiner Documentation''. Retrieved April 28, 2019.</ref><br />
<br />
==== Agglomerative Hierarchical Clustering ====<br />
Also known as AGNES (Agglomerative Nesting), the Agglomerative Hierarchical Clustering Technique also creates clusters based on similarity. To start, this method takes each object and treats it as if it were a cluster. It then merges each of these clusters in pairs, until one huge cluster consisting of all the individual clusters has been formed. The result is represented in the form of a tree, which is called a ''dendrogram''. The manner in which the algorithm works is called the "bottom-up" technique. Each data entry is considered an individual element or a leaf node. At each following stage, the element is joined with its closest or most-similar element to form a bigger element or parent node. The process is repeated over and over until the root node is formed, with all of the subsequent nodes under it.<br />
<br />
The opposite of this technique is through the "top-down" method, which is implemented in an algorithm called "Divisive Clustering". This method starts at the root node, and at each iteration nodes are split or "divided" into two separate nodes, based off the ranking of dissimilarity within the clusters. This is done until every node has been divided, leaving individual clusters or leaf nodes. <ref>[https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/ "HIERARCHICAL CLUSTERING IN R: THE ESSENTIALS/Agglomerative Hierarchical Clustering"]. ''DataNovia''. Retrieved April 28, 2019.</ref><br />
<br />
==== Deep Learning and Neural Networks ====<br />
Neural networks are a collection of algorithms that utilize many of the concepts mentioned above while taking their capabilities a step further through deep learning. On a high level, the purpose of a neural network is to interpret raw input data through a machine perception and return patterns in the data, through techniques above such as K-means clustering or Random Forests. To do so, a neural network requires datasets to train on and thus models its interpretations on the training sets in a machine learning process. Where neural networks differ is its ability to be “stacked” to engage in deep learning. Each process is held in nodes that can be likened to neurons in a human brain. When data is encountered, many separate computations occur that can be weighted to produce the desired output. How many “layers”, or the depth, of a neural network increases its capabilities and complexity multiplicatively. <ref> A Beginner's Guide to Neural Networks and Deep Learning. (n.d.). Retrieved April 27, 2019, from https://skymind.ai/wiki/neural-network </ref><br />
<br />
== Ethical Dilemmas ==<br />
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.<br />
<br />
=== Bias ===<br />
Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. <br />
<br />
====Joy Buolamwini and Facial Recognition====<br />
Joy Buolamwini, a graduate computer science student at MIT, experienced a case of this. The facial recognition software she was working on failed to detect her face, as she had a skin tone that had not been accounted for in the facial recognition algorithm. This is because the software had used machine learning with a dataset that was not diverse enough, and as a result, the algorithm failed to recognize her.<ref>Buolamwini, Joy. [www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/ "How I'm Fighting Bias in Algorithms"]. ''MIT Media Lab''. Retrieved April 28, 2019.</ref> Safiya Noble discusses instances of algorithmic search engines reinforcing racism in her book, "Algorithms of Oppression".<ref>Noble, Safiya. ''Algorithms of Oppression''.</ref> Bias like this occurs in countless algorithms, be it through insufficient machine learning data sets, or the algorithm developers own fault, among other reasons, and it has the potential to cause legitimate problems even outside the realm of ethics.<br />
<br />
====Bias in Criminalization====<br />
[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing COMPAS] is algorithm written to determine whether a criminal is likely to re-offend using information like age, gender, and previously committed crimes. Tests have found it to be more likely to incorrectly evaluate black people than white people because it has learned on historical criminal data, which has been influenced by our biased policing practices.<ref>Larson, Mattu; Kirchner, Angwin (May 23, 2016). [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm "How We Analyzed the COMPAS Recidivism Algorithm"]. ''Propublica''. Retrieved April 28, 2019.</ref> <br />
<br />
Jerry Kaplan is a research affiliate at Stanford University’s Center on Democracy, Development and the Rule of Law at the Freeman Spogli Institute for International Studies, where he teaches “Social and Economic Impact of Artificial Intelligence.” According to Kaplan, algorithmic bias can even influence whether or not a person is sent to jail. A 2016 study conducted by ProPublica indicated that software designed to predict the likelihood an arrestee will re-offend incorrectly flagged black defendants twice as frequently as white defendants in a decision-support system widely used by judges. Ideally, predictive systems should be wholly impartial and therefore be agnostic to skin color. However, surprisingly, the program cannot give black and white defendants who are otherwise identical the same risk score, and simultaneously match the actual recidivism rates for these two groups. This is because black defendants are re-arrested at higher rates that their white counterparts (52% versus 39%), at least in part due to racial profiling, inequities in enforcement, and harsher treatment of black people within the justice system. <ref>Kaplan, J. (December 17, 2018). [https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/ "Why your AI might be racist"]. ''Washington Post''. Retrieved April 28, 2019.</ref><br />
<br />
==== Job Applicants ====<br />
Many companies employ complex algorithms in order to review and sift the thousands of resumes they will receive each year. Sometimes these algorithms display a bias which can result in people with a specific racial background or gender being recommended over others. An example of this was an Amazon AI algorithm that preferred men over women in recommending people for an interview. The algorithm employed machine learning techniques and over time taught itself to prefer men over women due to a variety of factors <ref>Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women.” Reuters, Thomson Reuters, 9 Oct. 2018, af.reuters.com/.</ref>. A major problem facing machine learning algorithms is the unpredictability in their models and what they will begin to teach themselves. It was apparent Amazon engineers did not intend for their algorithm to be bias towards men but an error resulted in this happening. Additionally, this was not a quick fix as the algorithm had been in place for years and began to pick up this bias and after analysis after a period time, it was discovered.<br />
<br />
=== Privacy And Data Gathering ===<br />
The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref>Turilli, Matteo, and Luciano Floridi (2009). "The Ethics of Information Transparency." ''Ethics and Information Technology''. '''11'''(2): 105-112. doi:10.1007/s10676-009-9187-9.</ref> is an import point regarding these issues. In popular social media algorithms, user information is often probed without the knowledge of the individual, and this can lead to problems. It is often not transparent enough how these algorithms receive user data, resulting in often incorrect information which can affect both how a person is treated within social media, as well as how outside agents could view these individuals given false data. Algorithms can also often infringe on a user’s feelings of privacy, as data can be collected that a person would prefer to be private. Data brokers are in the business of collecting peoples in formation and selling it to anyone for a profit, like data brokers companies often have their own collection of data. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref>Griffin, Andrew (October 4, 2017) [http://www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html "Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth."] ''The Independent''. Retrieved April 28, 2019.</ref> The information leaked contained data relating to usernames, passwords, as well as dates of birth. Privacy and data gathering are common ethical dilemmas relating to algorithms and are often not considered thoroughly enough by algorithm’s users.<br />
<br />
===The Filter Bubble===<br />
[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]<br />
Algorithms can be used to filter results in order to prioritize items that the user might be interested in. On some platforms, like Amazon, people can find this filtering useful because of the useful shopping recommendations the algorithm can provide. However, in other scenarios, this algorithmic filtering can become a problem. For example, Facebook has an algorithm that re-orders the user's news feed. For a period of time, the technology company prioritized sponsored posts in their algorithm. This often prioritized news articles, but there was no certainty on whether these articles came from a reliable source, simply the fact that they were sponsored. Facebook also uses its technology to gather information about its users, like which political party they belong to. This combined with prioritizing news can create a Facebook feed filled with only one party's perspective. This phenomenon is called the filter bubble, which essentially creates a platform centered completely around its user's interests. <br />
<br />
Many, like Eli Pariser, have questioned the ethical implications of the filter bubble. Pariser believes that filter bubbles are a problem because they prevent users from seeing perspectives that might challenge their own. Even worse, Pariser emphasizes that this filter bubble is invisible, meaning that the people in it do not realize that they are in it. <ref>Pariser, Eli. (2012). ''The Filter Bubble''. Penguin Books.</ref> This creates a huge lack of awareness in the world, allowing people to stand by often uninformed opinions and creating separation, instead of collaboration, with users who have different beliefs. Because of the issues Pariser outlined, Facebook decided to change their algorithm in order to prioritize posts from friends and family, in hopes of eliminating the effects of the potential filter bubble.<br />
====Filter Bubble in Politics====<br />
Another issue that these Filter Bubbles create are echo chambers; Facebook, in particular, filters out [political] content that one might disagree with, or simply not enjoy <ref>Knight, Megan (November 30, 2018). [http://theconversation.com/explainer-how-facebook-has-become-the-worlds-largest-echo-chamber-91024 "Explainer: How Facebook Has Become the World's Largest Echo Chamber"]. ''The Conversation''. Retrieved April 28, 2019.</ref>. The more a user "likes" a particular type of content, similar content will continue to appear, and perhaps content that is even more extreme. This was clearly seen in the 2016 election when without realizing it, voters developed tunnel vision. Rarely did their Facebook comfort zones expose them to opposing views, and as a result they eventually became victims to their own biases and the biases embedded within the algorithms.<ref>El-Bermawy, Mostafa M. (June 3, 2017). [https://www.wired.com/2016/11/filter-bubble-destroying-democracy/ "Your Filter Bubble Is Destroying Democracy"]. ''Wired''. Retrieved April 28, 2019.</ref> Later studies produced visualizations to show how insular the country was at the time of the election on social media and the large divide between the two echo chambers with almost no ties to each other.<ref>MIS2: Misinformation and Misbehavior Mining on the Web - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 [accessed 23 Apr, 2019]</ref> <br />
<br />
[[File:Socal-med-polarizing.png|thumbnail|right|From [https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 Research Gate]: An example of algorithmic echo chambers that contribute to the polarization of political beliefs]]<br />
<br />
===Corrupt Personalization===<br />
Algorithms have the potential to become dangerous, with their most serious repercussions being the threat to democracy that is extensive personalization. Algorithms such as Facebook's are corrupt in the practice of "like recycling" that they partake in. In Christian Sandvig's article title ''Corrupt Personalization,'' he notes that Facebook has defined a "like" in two ways that the users do not realize. The first is that "anyone who clicks on a "like" button is considered to have "liked" all future content from that source," and the second is that "anyone who "likes" a comment on a shared link is considered to "like" wherever that link points to" <ref>Sandvig, Christian. “Corrupt Personalization.” Social Media Collective, 27 June 2014, socialmediacollective.org/2014/06/26/corrupt-personalization/</ref>. As a result, posts that you "like" wind up becoming ads on your friends' pages claiming that you like a certain item or thing. You are not able to see these posts and, because they do not appear on your news feed, you do not have the power to delete them. This becomes a threat to one's autonomy, for even if they wanted to delete this post, they can not. Furthermore, everyone is entitled to the ability to manage the public presentation of their own self-identity, and in this corrupt personalization, Facebook is giving users new aspects of their identity that may or may not be accurate. <br />
<br />
=== Agency And Accountability ===<br />
Algorithms make "decisions" based on the steps they were designed to follow and the input they received. This can often lead to algorithms as autonomous agents<ref>“Autonomous Agent.” Autonomous Agent - an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/computer-science/autonomous-agent.</ref>, taking decision making responsibilities out of the hands of real people. Useful in terms of efficiency, these autonomous agents are capable of making decisions in a greater frequency than humans. Efficiency is what materializes the baseline for algorithm use in the first place.<br />
<br />
From an ethical standpoint, this type of agency raises many complications, specifically regarding accountability. It is no secret that many aspects of life are run by algorithms. Even events like applying to jobs are often drastically effected by these processes. Information like age, race, status, along with other qualifications, are all fed to algorithms, which then take agency and decide who moves further along in the hiring process and who is left behind. Disregarding inherent biases in this specific scenario, this process still serves to reduce the input of real humans and decrease the number of decisions that they have to make, and what is left over is the fact that autonomous agents are making systematic decisions that have extraordinary impact on people's lives. While the results of the previous example may only culminate to the occasional disregard of a qualified applicant or resentful feelings, this same principle can be much more influential.<br />
<br />
====The Trolley Problem in Practice====<br />
Consider autonomous vehicles, or self-driving cars, for instance. These are highly advanced algorithms that are programmed to make split second decisions with the greatest possible accuracy. In the case of the well-known "Trolley Problem"<ref>Roff, Heather M. “The Folly of Trolleys: Ethical Challenges and Autonomous Vehicles.” Brookings, Brookings, 17 Dec. 2018, www.brookings.edu/research/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/.</ref>, these agents are forced to make a decision jeopardizing one party or another. This decision can easily conclude in the injury or even death of individuals, all at the discretion of a mere program. <br />
<br />
The issue of accountability is then raised, in a situation such as this, because in the eyes of the law, society, and ethical observers, there must be someone held responsible. Attempting to prosecute a program would not be feasible in a legal situation, due to not being able to have a physical representation of the program, like you would a person. However, there are those such as Frances Grodzinsky and Kirsten Martin <ref>Martin, Kirsten. “Ethical Implications and Accountability of Algorithms.” SpringerLink, Springer Netherlands, 7 June 2018, link.springer.com/article/10.1007/s10551-018-3921-3.</ref>, who believe that the designers of an artificial agent, should be responsible for the actions of the program. <ref name="Grodzinsky"> "The ethics of designing artificial agents" by Frances S. Grodzinsky et al, Springer, 2008.</ref> Others contend this point by saying that the blame should be attributed to the users or persons directly involved in the situation. <br />
<br />
These complications will continue to arise, especially as algorithms continue to make autonomous decisions at grander scales and rates. Determining responsibility for the decisions these agents make will continue to be a vexing process, and will no doubt shape in some form many of the advanced algorithms that will be developed in the coming years.<br />
<br />
=== Intentions and Consequences ===<br />
The ethical consequences that are common in algorithm implementations can be either deliberate or unintentional. Instances where an algorithm's intent and outcome differs is noted below. <br />
<br />
==== YouTube Radicalization ====<br />
Scholar and technosociologist Zeynep Tufekci has claimed that "[[YouTube]] may be one of the most powerful radicalizing instruments of the 21st century."<ref name="YouTube Radicalization">[https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html] Tufekci, Zeynep. “YouTube, the Great Radicalizer.” The New York Times, The New York Times, 10 Mar. 2018, www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html.</ref> As YouTube algorithms aim maximize the amount of time that viewers spend watching, it inevitably discovered that the best way to do this was to show videos that slowly "up the stakes" of the subject being watched - from jogging to ultramarathons, from vegetarianism to veganism, from Trump speeches to white supremacist rants.<ref name="YouTube Radicalization" /> Thus, while the intention of Youtube is to keep viewers watching (and bring in advertisement money), they have unintentionally created a site that shows viewers more and more extreme content - contributing to radicalization. Such activity circles back to and produce filters and echo chambers.<br />
<br />
==== Facebook Advertising ====<br />
By taking a closer look at Facebook's algorithm that serves up ads to its users, gender and racial bias is obviously prominent. Using demographic and racial background as factors, Facebook's decides which ads are served up to its users. A team from Northeastern tested to see the algorithm bias in action and by running identical ads with slight tweaks to budget, images, and headings, the ads reached vastly different audiences. Results such as minorities receiving a higher percentage of low-cost housing ads, and women receiving more ads for secretary and nursing jobs <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Although the intent of Facebook may be to reach people that these ads are intended for, the companies that are signing up to advertise with Facebook have stated they did not anticipate this type of filtering when paying for their services. Additionally, although Facebook may believe it is win-win for everyone since advertisers will be getting more interactions with certain audiences targeted, and people will be happy to see ads more relatable to them shown, it is incredibly discriminatory to target people based on factors that are uncontrollable. Facebook needs to adjust its targeted advertising practices by removing racial and gender factors in their algorithms in order to prevent perpetuating stereotypes and placing people in certain boxes by race and gender. This type of algorithm goes against many ethical principles and is important that powerful technology companies are not setting poor examples for others.<br />
<br />
==See also==<br />
{{resource|<br />
*[[Bias in Information]]<br />
*[[Artificial Agents]]<br />
*[[Value Sensitive Design]]<br />
*[[Artificial Intelligence and Technology]]<br />
}}<br />
<br />
== References ==<br />
<references/><br />
[[Category:2019New]]<br />
[[Category:Concepts]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Algorithms&diff=91441Algorithms2021-02-11T17:53:37Z<p>WikiSysop: Reverted edits by WikiSysop (talk) to last revision by Nlampa</p>
<hr />
<div>[[File:Algorithm.png|200px|thumb|right]]<br />
{{Nav-Bar|Topics##}}<br><br />
An '''Algorithm''' is defined as a set of precise steps and distinct states used to express the detailed structure of a program or the order of events that occurred in a system <ref>Cormen, Thomas H. et al. (2009). ''Introduction to Algorithms;'. MIT Press.</ref>.Algorithms are involved in many aspects of daily life and in complex computer science concepts. They often use repetition of operations to allow people and machines to execute tasks more efficiently by executing tasks faster and using fewer resources such as memory. On a basic level, an algorithm is a system working through different iterations of a process<ref>Lim, Brian (December 7, 2016). [e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/ "A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life)"] ''e27''.</ref>. They can help turn systematic and tedious tasks into fast, automated processes. Large companies particularly value robust algorithms because their infrastructure depends on efficiency to remain profitable on a massive scale<ref>Rastogi, Rajeev, and Kyuseok Shim (1999). "Scalable algorithms for mining large databases." ''Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining''. ACM.</ref>.<br />
<br />
Processes like cooking a meal or reading a manual to assemble a new piece of furniture are examples of algorithms in everyday life<ref>T.C. (August 29, 2017). [https://www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms "What Are Algorithms?"] ''The Economist''. Retrieved April 28, 2019.</ref>. Algorithms are grounded in logic. The increase in their logical complexity via advancements in technology and human effort have provided the foundations for technological concepts such as artificial intelligence and machine learning<ref>McClelland, Calum. (December 4, 2017). [https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991 "The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning"]. ''Medium''. Retrieved April 28, 2019.</ref>. The influence of algorithms is pervasive and in computer science so it leads to an increase in ethical concerns in the areas of bias, privacy, and accountability.<br />
<br />
<br />
== History ==<br />
The earliest known algorithms stem back to 1700 BC when the Egyptians created them for quick multiplication and division<ref>Stepanov, A. and Rose, D. (2014).<br />
''From Mathematics to Generic Programming: The First Algorithm''. [http://www.informit.com/articles/article.aspx?p=2264460 "Chapter 2"]. Retrieved April 28, 2019.</ref>. Since then, ancient Babylonians in 300 BC created algorithms to track farming and livestock using square roots. Following this, steady advancement gave birth to fundamental mathematical algorithm families like algebra, shaping the field of mathematics with its all-purpose formulas. The man often accredited as “The Father of Algebra,” Muhammad ibn Mūsa al-Khwarizmī, was also the one who gave English the word “algorithm” around 850 AD, as he wrote a book ''Al-Khwarizmi on the Hindu Art of Reckoning'', which in Latin translates to ''Algoritmi de Numero Indorum''. The English word [https://www.digit.in/features/science-and-technology/the-origin-of-algorithms-30045.html "algorithm"] was adopted from this title.<br />
<br />
A myriad of fundamental algorithms have been developed throughout history, ranging from pure mathematics to important computer science stalwarts, extending from ancient times up through the modern day. The computer revolution led to algorithms that can filter and personalize results based on the user.<br />
<br />
The [https://en.wikipedia.org/wiki/Timeline_of_algorithms Algorithm Timeline] outlines the advancements in the development of the algorithm as well as a number of the well-known algorithms developed from the Medieval Period until modern day.<br />
<br />
=== Computation ===<br />
Another cornerstone for algorithms comes from [https://en.wikipedia.org/wiki/Alan_Turing Alan Turing] and his contributions to cognitive and [https://en.wikipedia.org/wiki/Computer_science computer science]. Turing conceptualized the concept of cognition and designed ways to emulate human cognition with machines. This process turned the human thought process into mathematical algorithms and it led to the development of Turing Machines. It capitalized on these theoretical algorithms to perform unique functions and the development of computers. As their name suggests, computers utilized specific rules or algorithms to compute and it is these machines (or sometimes people)<ref>[crgis.ndc.nasa.gov/historic/Human_Computers "Human Computers"]. ''NASA Cultural Resources''. Retrieved April 28, 2019.</ref> that most often relate to the concept of algorithms that is used today. With the advent of mechanical computers, the computer science field paved the way for algorithms to run the world as they do now by calculating and controlling an immense quantity of facets of daily life. To this day, Turing machines are a main area of study in the theory of computation.<br />
<br />
=== Advancements In Algorithms ===<br />
In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity. Advanced algorithms, such as artificial intelligence is defined as utilizing machine learning capabilities.<ref>Anyoha, Rockwell (August 28, 2017). [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ "The History of Artificial Intelligence"]. ''Science in the News''. Harvard. Retrieved April 28, 2019.</ref> This level of algorithmic improvement provided the foundation for more technological advancement.<br />
[[File:machineLearning.png|500px]]<br />
<br />
The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.<br />
<br />
== Classifications ==<br />
There are many different classifications of algorithms, some are more well-suited for particular families of computational problems than others. In many cases, the algorithm one chooses to make for a given problem will have tradeoffs dealing with time complexity and memory usage.<br />
<br />
==== Recursive Algorithms ====<br />
<br />
A [https://en.wikipedia.org/wiki/Recursion_(computer_science) recursive algorithm] is an algorithm that calls itself with decreasing values in order to reach a pre-defined base case solution. The base case solution determines the values that are sent back up the recursive stack to determine the final outcome of the algorithm. It follows the principle of solving subproblems to solve the larger problem since once the base case solution is reached, the algorithm works upwards to fit the solution into the larger subproblem. The base case must be present, otherwise the recursive function will never stop calling itself, creating an infinite loop. Since recursion involves numerous function calls, it is one of the main sources of stack overflow. With recursive calls, programs have to save more stacks despite a lack of available space. Further, some recursive functions require additional computations even after the recursive call, adding to the consumption speed and memory. 'Tail Recursive' functions are an efficient solution to this, wherein recursive calls happen at the very end of the function, allowing only one stack to be saved throughout the function calls.<br />
<br />
Due to the recurring memory stack frames that are created with each call, recursive algorithms generally require more memory and computation power. However, they are still viewed as simplistic and succinct ways to write elaborate algorithms. <br />
<br />
==== Serial, Parallel or Distributed ====<br />
A [https://en.wikipedia.org/wiki/Sequential_algorithm serial algorithm] is an algorithm in which calculation is done in sequential manners on one core, it follows a defined order in solving a problem. Parallel algorithms utilizes the fact that modern computers have more than one cores, so that computations that are not interdependent could be calculated on separate cores. This is referred to as multi-threaded algorithm where each thread is a series of executing commands and they are inter-weaved to ensure correct output without deadlock. A deadlock occurs when there are interdependence between more than one thread so that none of the threads can continue until one of the other threads continues. Parallel algorithm is important that it allows more than one program to run at the same time by leveraging a computer's available resources otherwise would not have been possible with serial algorithm. Finally, distributed algorithm is similar to parallel algorithm in that it allows multiple programs to run at once with the exception that instead of leveraging multiple cores in a single computer it leverages multiple computers that communicates through a computer network. Similar to how parallel algorithm builds on serial algorithm with the added complexity of synchronizing threads to prevent deadlock and ensure correct outputs, distributed algorithm builds on parallel algorithm with the added complexity of managing communication latency and defining order since it is impossible to synchronize every computer's clock in a distributed system without significant compromises. A distributed algorithm provides extra reliability in that data are stored in more than one location so that one failure would not result in loss of data and by doing computation in multiple computer it can potentially have even faster computational speed.<br />
<br />
==== Deterministic vs Non-Deterministic ====<br />
Deterministic algorithms are those that solve problems with exact precision and ordering so a given input would produce the same output every time. Non-deterministic algorithms either have data races or utilize certain randomization, so the same input could have a myriad of outputs. Non-deterministic algorithms can be represented by flowcharts, programming languages, and machine language programs. However, they vary in that they are capable of using a multiple-valued function whose values are the positive integers less than or equal to itself. In addition, all points of termination are labeled as successes or failures. The terminology "non-deterministic" does not imply randomness, of rather a kind of free will<ref>"<br />
Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref><br />
<br />
==== Exact vs Approximation ====<br />
Some algorithms are implemented to solve for the exact solutions of a problem, whereas some problems are implemented for an approximation or heuristic. Approximation is important in which heuristics could provide an answer that is good enough such that the excessive computational time necessary to find the actual solution is not warranted since one would get minimal gains while expanding a great deal of resources. An example where an approximation is warranted is the traveling salesman's algorithm in which has computational complexity of O(n!), so an heuristic is necessary since some high values of n are not even possible to calculate.<br />
<br />
==== Brute Force or Exhaustive Search ====<br />
A brute force algorithm is the most "naive" approach one can take in attempting to solve a particular problem. A solution is reached by searching through every single possible outcome before arriving at an answer. In terms of complexity or Big-O notation, brute force algorithms typically represent the highest order complexity compared to other potential solutions for a given problem. While brute force algorithms may not be considered the most efficient option for solving computational problems, they do offer reliability as well as a guarantee that a solution to a given problem will eventually be found.<br />
<br />
An example of a brute force algorithm would be trying all combinations of a 4-digit passcode, in order to crack into a target's smartphone.<br />
<br />
==== Divide and Conquer ====<br />
A divide and conquer algorithm divides a problem into smaller sub-problems then conquer each smaller problems before merging them together to solve the original problem. In terms of efficiency and the Big-O notation, the Divide and Conquer fares better than Brute Force but is still relatively inefficient compared to other more complex algorithms. An example of divide and conquer is merge sort wherein a list is split into smaller sorted lists and then merged together to sort the original list.<br />
<br />
Examples of Divide and Conquer algorithms would be the sorting algorithm [https://en.wikipedia.org/wiki/Merge_sort Merge Sort], and the searching algorithm [https://en.wikipedia.org/wiki/Binary_search_algorithm Binary Search]<ref>[https://www.geeksforgeeks.org/divide-and-conquer-algorithm-introduction "Divide and Conquer Algorithms"]. ''Geeks for Geeks''. Retrieved April 28, 2019.</ref>.<br />
<br />
====Dynamic Programming====<br />
[https://.wikipedia.org/wiki/Dynamic_programming Dynamic programming] takes advantage of overlapping subproblems to more efficiently solve a larger computational problem. The algorithm first solves less complex subproblems and stores their solutions in memory. Then more complex problems will find these solutions using some method of lookup to find the solution and implement it in the more complex problem to find its own solution. The method of lookup enables solutions to be computed once and used multiple times. This method reduces the time complexity from exponential to polynomial.<br />
<br />
An example of a common problem that can be solved by Dynamic Programming is the [https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm 0-1 Knapsack Problem].<br />
<br />
==== Backtracking ====<br />
A backtracking algorithm is similar to brute force with the exception that as soon as it reaches a node where a solution could never be reached from said node on, it prunes all the subsequent node and backtracks to the closest node that has the possibility to be right. Pruning in this context means neglecting the failed branch as a potential solution branch in all further searches, reducing the scope of the possible solution set and eventually guiding the program to the right outcome.<br />
<br />
An example of some problems that can be solved by algorithms that take advantage of backtracking is solving [[Wikipedia:Sudoku|Sudoko]], or the [[Wikipedia:Eight_queens_puzzle|N-Queens Problem]].<br />
<br />
====Greedy Algorithm====<br />
An intuitive approach to design algorithms that don’t always yield the optimal solution. This approach required a collection of candidates, or options, in which the algorithm selects in order to satisfy a given predicate. Greedy algorithms can either favor the least element in the collection or the greatest in order to satisfy the predicate<ref>[https://brilliant.org/wiki/greedy-algorithm/ "Greedy Algorithms"]. ''Brilliant Math & Science Wiki''. Retrieved April 28, 2019.</ref>.<br />
<br />
An example of a greedy algorithm may take the form of selecting coins to make change in a transaction. The collection includes the official coins of the U.S. currency (25 cents, 10 cents, 5 cents, and 1 cent) and the predicate would be to make change of 11 cents. The greedy algorithm will select the greatest of our collection to approach 11, but not past it. The algorithm will first select a dime, then a penny, then end when 11 cents has been made.<br />
<br />
== Complexity and Big-O Notation ==<br />
[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]<br />
Measuring the efficiency of an algorithm is standardized by checking how well it grows with more inputs. Computer scientists calculate how much computational time increases with an increasing number of inputs. Since this form of measurement merely intends to see how well an algorithm grows, the constants are left out since with high inputs these constants are negligible anyways. Big-O notation specifically describes the worst-case scenario and measures the time or space the algorithm uses<ref name = 'Big-O'>[ Bell, Rob. [https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/ "A Beginner's Guide to Big O Notation"]. Retrieved April 28, 2019.</ref>. Big-O notation can be broken down into order of growth algorithms such as O(1), O(N), and O(log N), with each notation representing different orders of growth. The later, log algorithms -commonly referred to as logarithms, are bit more complex than the rest, log algorithms take a median from a data set and compare it to a target value, the algorithm continues to halve the data as long as the median is higher or lower than the target value<ref name='Big-O'></ref>. An algorithm with a higher Big-O is less efficient at large scales, for example in general a O(N) algorithm will run slower than a O(1) algorithm, and this difference will be more and more apparent, the larger the number of inputs.<br />
<br />
== Artificial Intelligence Algorithms ==<br />
=== Clustering ===<br />
Clustering is a [https://en.wikipedia.org/wiki/Machine_learning Machine Learning] technique in which, data is segregated into groups called clusters through an algorithm, given a set of data points. These clustering algorithms classify the data based on various criteria, but the fundamental premise is that data points with similarities will belong in the same group, that must be dissimilar to other groups. There are numerous clustering algorithms including K-Means clustering, Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering, EM (Expectation Maximization) Clustering, Agglomerative Hierarchical Clustering. <ref name="Clustering">Seif, George (February 5, 2018). [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 "The 5 Clustering Algorithms Data Scientists Need To Know"]. Retrieved April 28, 2019.</ref><br />
<br />
==== K-Means Clustering ====<br />
K-means clustering is the most widely known and used algorithm out of all the clustering algorithms. It involves pre-determining a target number - ''k'', which represents the number of centroids needed in the dataset. A centroid refers to the predicted center of the cluster. It then identifies the data points nearest to the center to form each cluster, while keeping ''k'' as small as possible. <ref>Garbade, Michael J. (September 12, 2018). [https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 "Understanding K-Means Clustering in Machine Learning"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> K-Means clustering is considered to be a fast algorithm, due to the minimal computations it requires. It has a Big-O complexity of O(''n''). <ref name="Clustering"></ref> <br />
<br />
==== Mean-Shift Clustering ====<br />
Mean-Shift Clustering, also known as Mode-Seeking, is an algorithm where datapoints are grouped into clusters, through the process of iteratively shifting all the points towards their mode. The mode of a dataset is defined as the most occurring value in that particular dataset, or in graphical terms, the point where the density of data points is the highest. Therefore, the algorithm moves, or "shifts" each point closer to its closest centroid, the direction of which is determined by the density of the nearby points. Therefore, each iteration of the program moves each point closer to where all the other points are, eventually forming a cluster center. The key difference between Mean-Shift and K-Means clustering is that K-Means requires the number ''k'' to be set beforehand, whereas the Mean-Shift algorithm creates clusters on the go without necessarily determining how many will be formed. <ref>[http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/ "Meanshift Algorithm for the Rest of Us (Python)"], May 14, 2016. Retrieved April 28, 2019.</ref> Usually, the Big-O complexity of such an algorithm is O(''Tn^2''), where ''T'' refers to the number of iterations in the algorithm. <ref>Thirumuruganathan, Saravanan (April 1, 2010). [https://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/ "Introduction To Mean Shift Algorithm"]. Retrieved April 28, 2019.</ref><br />
<br />
==== DBSCAN Clustering ====<br />
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that groups together nearby datapoints based on a measure of distance, often [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] and a minimal number of points. The algorithm also differentiates outliers if they are in low density areas. The algorithm requires two parameters - ''eps'' and ''minPoints''. The decision on what to set these parameters as depends from dataset to dataset, and requires a fundamental understanding of the context of the dataset being used. The ''eps'' should be picked based on the dataset distance and while generally small ''eps'' values are desirable, if the set value is too small there is a danger that a portion of the data will go unclustered. Conversely, if the value set is too large, too many of the points might get grouped into the same cluster. The minPoints parameter is usually derived from the parameter ''D'', following that minPoints ≥ ''D + 1'' where ''D'' measures the number of dimensions in the data. <ref>Salton do Prado, Kelvin (April 1, 2017). [https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80 "How DBSCAN works and why should we use it?"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> The average run-time complexity of a DBSCAN algorithm is O(''n log n'') whereas it's worst-case complexity can be O(''n^2'').<br />
<br />
==== EM Clustering ====<br />
Expectation Maximization or EM Clustering is similar to the K-Means clustering technique. The EM Clustering method takes forward the basic principles of K-Means Clustering in two primary ways:<br />
1) The EM Clustering method aims to calculate what datapoints belong in what cluster through one or more probability distributions, instead of trying to simply calculate and maximize the difference in mean points.<br />
2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.<br />
<br />
Essentially, the EM clustering method approximates the distribution of each point based on different probability distributions and at the end of it, each observation has a certain level of probability of belonging to a particular cluster. Ultimately, the resulting clusters are analyzed based on which clusters have the highest classification probabilities. <ref>[https://docs.rapidminer.com/latest/studio/operators/modeling/segmentation/expectation_maximization_clustering.html "Expectation Maximization Clustering"] ''RapidMiner Documentation''. Retrieved April 28, 2019.</ref><br />
<br />
==== Agglomerative Hierarchical Clustering ====<br />
Also known as AGNES (Agglomerative Nesting), the Agglomerative Hierarchical Clustering Technique also creates clusters based on similarity. To start, this method takes each object and treats it as if it were a cluster. It then merges each of these clusters in pairs, until one huge cluster consisting of all the individual clusters has been formed. The result is represented in the form of a tree, which is called a ''dendrogram''. The manner in which the algorithm works is called the "bottom-up" technique. Each data entry is considered an individual element or a leaf node. At each following stage, the element is joined with its closest or most-similar element to form a bigger element or parent node. The process is repeated over and over until the root node is formed, with all of the subsequent nodes under it.<br />
<br />
The opposite of this technique is through the "top-down" method, which is implemented in an algorithm called "Divisive Clustering". This method starts at the root node, and at each iteration nodes are split or "divided" into two separate nodes, based off the ranking of dissimilarity within the clusters. This is done until every node has been divided, leaving individual clusters or leaf nodes. <ref>[https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/ "HIERARCHICAL CLUSTERING IN R: THE ESSENTIALS/Agglomerative Hierarchical Clustering"]. ''DataNovia''. Retrieved April 28, 2019.</ref><br />
<br />
==== Deep Learning and Neural Networks ====<br />
Neural networks are a collection of algorithms that utilize many of the concepts mentioned above while taking their capabilities a step further through deep learning. On a high level, the purpose of a neural network is to interpret raw input data through a machine perception and return patterns in the data, through techniques above such as K-means clustering or Random Forests. To do so, a neural network requires datasets to train on and thus models its interpretations on the training sets in a machine learning process. Where neural networks differ is its ability to be “stacked” to engage in deep learning. Each process is held in nodes that can be likened to neurons in a human brain. When data is encountered, many separate computations occur that can be weighted to produce the desired output. How many “layers”, or the depth, of a neural network increases its capabilities and complexity multiplicatively. <ref> A Beginner's Guide to Neural Networks and Deep Learning. (n.d.). Retrieved April 27, 2019, from https://skymind.ai/wiki/neural-network </ref><br />
<br />
== Ethical Dilemmas ==<br />
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.<br />
<br />
=== Bias ===<br />
Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. <br />
<br />
====Joy Buolamwini and Facial Recognition====<br />
Joy Buolamwini, a graduate computer science student at MIT, experienced a case of this. The facial recognition software she was working on failed to detect her face, as she had a skin tone that had not been accounted for in the facial recognition algorithm. This is because the software had used machine learning with a dataset that was not diverse enough, and as a result, the algorithm failed to recognize her.<ref>Buolamwini, Joy. [www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/ "How I'm Fighting Bias in Algorithms"]. ''MIT Media Lab''. Retrieved April 28, 2019.</ref> Safiya Noble discusses instances of algorithmic search engines reinforcing racism in her book, "Algorithms of Oppression".<ref>Noble, Safiya. ''Algorithms of Oppression''.</ref> Bias like this occurs in countless algorithms, be it through insufficient machine learning data sets, or the algorithm developers own fault, among other reasons, and it has the potential to cause legitimate problems even outside the realm of ethics.<br />
<br />
====Bias in Criminalization====<br />
[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing COMPAS] is algorithm written to determine whether a criminal is likely to re-offend using information like age, gender, and previously committed crimes. Tests have found it to be more likely to incorrectly evaluate black people than white people because it has learned on historical criminal data, which has been influenced by our biased policing practices.<ref>Larson, Mattu; Kirchner, Angwin (May 23, 2016). [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm "How We Analyzed the COMPAS Recidivism Algorithm"]. ''Propublica''. Retrieved April 28, 2019.</ref> <br />
<br />
Jerry Kaplan is a research affiliate at Stanford University’s Center on Democracy, Development and the Rule of Law at the Freeman Spogli Institute for International Studies, where he teaches “Social and Economic Impact of Artificial Intelligence.” According to Kaplan, algorithmic bias can even influence whether or not a person is sent to jail. A 2016 study conducted by ProPublica indicated that software designed to predict the likelihood an arrestee will re-offend incorrectly flagged black defendants twice as frequently as white defendants in a decision-support system widely used by judges. Ideally, predictive systems should be wholly impartial and therefore be agnostic to skin color. However, surprisingly, the program cannot give black and white defendants who are otherwise identical the same risk score, and simultaneously match the actual recidivism rates for these two groups. This is because black defendants are re-arrested at higher rates that their white counterparts (52% versus 39%), at least in part due to racial profiling, inequities in enforcement, and harsher treatment of black people within the justice system. <ref>Kaplan, J. (December 17, 2018). [https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/ "Why your AI might be racist"]. ''Washington Post''. Retrieved April 28, 2019.</ref><br />
<br />
==== Job Applicants ====<br />
Many companies employ complex algorithms in order to review and sift the thousands of resumes they will receive each year. Sometimes these algorithms display a bias which can result in people with a specific racial background or gender being recommended over others. An example of this was an Amazon AI algorithm that preferred men over women in recommending people for an interview. The algorithm employed machine learning techniques and over time taught itself to prefer men over women due to a variety of factors <ref>Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women.” Reuters, Thomson Reuters, 9 Oct. 2018, af.reuters.com/.</ref>. A major problem facing machine learning algorithms is the unpredictability in their models and what they will begin to teach themselves. It was apparent Amazon engineers did not intend for their algorithm to be bias towards men but an error resulted in this happening. Additionally, this was not a quick fix as the algorithm had been in place for years and began to pick up this bias and after analysis after a period time, it was discovered.<br />
<br />
=== Privacy And Data Gathering ===<br />
The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref>Turilli, Matteo, and Luciano Floridi (2009). "The Ethics of Information Transparency." ''Ethics and Information Technology''. '''11'''(2): 105-112. doi:10.1007/s10676-009-9187-9.</ref> is an import point regarding these issues. In popular social media algorithms, user information is often probed without the knowledge of the individual, and this can lead to problems. It is often not transparent enough how these algorithms receive user data, resulting in often incorrect information which can affect both how a person is treated within social media, as well as how outside agents could view these individuals given false data. Algorithms can also often infringe on a user’s feelings of privacy, as data can be collected that a person would prefer to be private. Data brokers are in the business of collecting peoples in formation and selling it to anyone for a profit, like data brokers companies often have their own collection of data. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref>Griffin, Andrew (October 4, 2017) [http://www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html "Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth."] ''The Independent''. Retrieved April 28, 2019.</ref> The information leaked contained data relating to usernames, passwords, as well as dates of birth. Privacy and data gathering are common ethical dilemmas relating to algorithms and are often not considered thoroughly enough by algorithm’s users.<br />
<br />
===The Filter Bubble===<br />
[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]<br />
Algorithms can be used to filter results in order to prioritize items that the user might be interested in. On some platforms, like Amazon, people can find this filtering useful because of the useful shopping recommendations the algorithm can provide. However, in other scenarios, this algorithmic filtering can become a problem. For example, Facebook has an algorithm that re-orders the user's news feed. For a period of time, the technology company prioritized sponsored posts in their algorithm. This often prioritized news articles, but there was no certainty on whether these articles came from a reliable source, simply the fact that they were sponsored. Facebook also uses its technology to gather information about its users, like which political party they belong to. This combined with prioritizing news can create a Facebook feed filled with only one party's perspective. This phenomenon is called the filter bubble, which essentially creates a platform centered completely around its user's interests. <br />
<br />
Many, like Eli Pariser, have questioned the ethical implications of the filter bubble. Pariser believes that filter bubbles are a problem because they prevent users from seeing perspectives that might challenge their own. Even worse, Pariser emphasizes that this filter bubble is invisible, meaning that the people in it do not realize that they are in it. <ref>Pariser, Eli. (2012). ''The Filter Bubble''. Penguin Books.</ref> This creates a huge lack of awareness in the world, allowing people to stand by often uninformed opinions and creating separation, instead of collaboration, with users who have different beliefs. Because of the issues Pariser outlined, Facebook decided to change their algorithm in order to prioritize posts from friends and family, in hopes of eliminating the effects of the potential filter bubble.<br />
====Filter Bubble in Politics====<br />
Another issue that these Filter Bubbles create are echo chambers; Facebook, in particular, filters out [political] content that one might disagree with, or simply not enjoy <ref>Knight, Megan (November 30, 2018). [http://theconversation.com/explainer-how-facebook-has-become-the-worlds-largest-echo-chamber-91024 "Explainer: How Facebook Has Become the World's Largest Echo Chamber"]. ''The Conversation''. Retrieved April 28, 2019.</ref>. The more a user "likes" a particular type of content, similar content will continue to appear, and perhaps content that is even more extreme. This was clearly seen in the 2016 election when without realizing it, voters developed tunnel vision. Rarely did their Facebook comfort zones expose them to opposing views, and as a result they eventually became victims to their own biases and the biases embedded within the algorithms.<ref>El-Bermawy, Mostafa M. (June 3, 2017). [https://www.wired.com/2016/11/filter-bubble-destroying-democracy/ "Your Filter Bubble Is Destroying Democracy"]. ''Wired''. Retrieved April 28, 2019.</ref> Later studies produced visualizations to show how insular the country was at the time of the election on social media and the large divide between the two echo chambers with almost no ties to each other.<ref>MIS2: Misinformation and Misbehavior Mining on the Web - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 [accessed 23 Apr, 2019]</ref> <br />
<br />
[[File:Socal-med-polarizing.png|thumbnail|right|From [https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 Research Gate]: An example of algorithmic echo chambers that contribute to the polarization of political beliefs]]<br />
<br />
===Corrupt Personalization===<br />
Algorithms have the potential to become dangerous, with their most serious repercussions being the threat to democracy that is extensive personalization. Algorithms such as Facebook's are corrupt in the practice of "like recycling" that they partake in. In Christian Sandvig's article title ''Corrupt Personalization,'' he notes that Facebook has defined a "like" in two ways that the users do not realize. The first is that "anyone who clicks on a "like" button is considered to have "liked" all future content from that source," and the second is that "anyone who "likes" a comment on a shared link is considered to "like" wherever that link points to" <ref>Sandvig, Christian. “Corrupt Personalization.” Social Media Collective, 27 June 2014, socialmediacollective.org/2014/06/26/corrupt-personalization/</ref>. As a result, posts that you "like" wind up becoming ads on your friends' pages claiming that you like a certain item or thing. You are not able to see these posts and, because they do not appear on your news feed, you do not have the power to delete them. This becomes a threat to one's autonomy, for even if they wanted to delete this post, they can not. Furthermore, everyone is entitled to the ability to manage the public presentation of their own self-identity, and in this corrupt personalization, Facebook is giving users new aspects of their identity that may or may not be accurate. <br />
<br />
=== Agency And Accountability ===<br />
Algorithms make "decisions" based on the steps they were designed to follow and the input they received. This can often lead to algorithms as autonomous agents<ref>“Autonomous Agent.” Autonomous Agent - an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/computer-science/autonomous-agent.</ref>, taking decision making responsibilities out of the hands of real people. Useful in terms of efficiency, these autonomous agents are capable of making decisions in a greater frequency than humans. Efficiency is what materializes the baseline for algorithm use in the first place.<br />
<br />
From an ethical standpoint, this type of agency raises many complications, specifically regarding accountability. It is no secret that many aspects of life are run by algorithms. Even events like applying to jobs are often drastically effected by these processes. Information like age, race, status, along with other qualifications, are all fed to algorithms, which then take agency and decide who moves further along in the hiring process and who is left behind. Disregarding inherent biases in this specific scenario, this process still serves to reduce the input of real humans and decrease the number of decisions that they have to make, and what is left over is the fact that autonomous agents are making systematic decisions that have extraordinary impact on people's lives. While the results of the previous example may only culminate to the occasional disregard of a qualified applicant or resentful feelings, this same principle can be much more influential.<br />
<br />
====The Trolley Problem in Practice====<br />
Consider autonomous vehicles, or self-driving cars, for instance. These are highly advanced algorithms that are programmed to make split second decisions with the greatest possible accuracy. In the case of the well-known "Trolley Problem"<ref>Roff, Heather M. “The Folly of Trolleys: Ethical Challenges and Autonomous Vehicles.” Brookings, Brookings, 17 Dec. 2018, www.brookings.edu/research/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/.</ref>, these agents are forced to make a decision jeopardizing one party or another. This decision can easily conclude in the injury or even death of individuals, all at the discretion of a mere program. <br />
<br />
The issue of accountability is then raised, in a situation such as this, because in the eyes of the law, society, and ethical observers, there must be someone held responsible. Attempting to prosecute a program would not be feasible in a legal situation, due to not being able to have a physical representation of the program, like you would a person. However, there are those such as Frances Grodzinsky and Kirsten Martin <ref>Martin, Kirsten. “Ethical Implications and Accountability of Algorithms.” SpringerLink, Springer Netherlands, 7 June 2018, link.springer.com/article/10.1007/s10551-018-3921-3.</ref>, who believe that the designers of an artificial agent, should be responsible for the actions of the program. <ref name="Grodzinsky"> "The ethics of designing artificial agents" by Frances S. Grodzinsky et al, Springer, 2008.</ref> Others contend this point by saying that the blame should be attributed to the users or persons directly involved in the situation. <br />
<br />
These complications will continue to arise, especially as algorithms continue to make autonomous decisions at grander scales and rates. Determining responsibility for the decisions these agents make will continue to be a vexing process, and will no doubt shape in some form many of the advanced algorithms that will be developed in the coming years.<br />
<br />
=== Intentions and Consequences ===<br />
The ethical consequences that are common in algorithm implementations can be either deliberate or unintentional. Instances where an algorithm's intent and outcome differs is noted below. <br />
<br />
==== YouTube Radicalization ====<br />
Scholar and technosociologist Zeynep Tufekci has claimed that "[[YouTube]] may be one of the most powerful radicalizing instruments of the 21st century."<ref name="YouTube Radicalization">[https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html] Tufekci, Zeynep. “YouTube, the Great Radicalizer.” The New York Times, The New York Times, 10 Mar. 2018, www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html.</ref> As YouTube algorithms aim maximize the amount of time that viewers spend watching, it inevitably discovered that the best way to do this was to show videos that slowly "up the stakes" of the subject being watched - from jogging to ultramarathons, from vegetarianism to veganism, from Trump speeches to white supremacist rants.<ref name="YouTube Radicalization" /> Thus, while the intention of Youtube is to keep viewers watching (and bring in advertisement money), they have unintentionally created a site that shows viewers more and more extreme content - contributing to radicalization. Such activity circles back to and produce filters and echo chambers.<br />
<br />
==== Facebook Advertising ====<br />
By taking a closer look at Facebook's algorithm that serves up ads to its users, gender and racial bias is obviously prominent. Using demographic and racial background as factors, Facebook's decides which ads are served up to its users. A team from Northeastern tested to see the algorithm bias in action and by running identical ads with slight tweaks to budget, images, and headings, the ads reached vastly different audiences. Results such as minorities receiving a higher percentage of low-cost housing ads, and women receiving more ads for secretary and nursing jobs <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Although the intent of Facebook may be to reach people that these ads are intended for, the companies that are signing up to advertise with Facebook have stated they did not anticipate this type of filtering when paying for their services. Additionally, although Facebook may believe it is win-win for everyone since advertisers will be getting more interactions with certain audiences targeted, and people will be happy to see ads more relatable to them shown, it is incredibly discriminatory to target people based on factors that are uncontrollable. Facebook needs to adjust its targeted advertising practices by removing racial and gender factors in their algorithms in order to prevent perpetuating stereotypes and placing people in certain boxes by race and gender. This type of algorithm goes against many ethical principles and is important that powerful technology companies are not setting poor examples for others.<br />
<br />
==See also==<br />
{{resource|<br />
*[[Bias in Information]]<br />
*[[Artificial Agents]]<br />
*[[Value Sensitive Design]]<br />
*[[Artificial Intelligence and Technology]]<br />
}}<br />
<br />
== References ==<br />
<references/><br />
[[Category:2019New]]<br />
[[Category:Concepts]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Algorithms&diff=91440Algorithms2021-02-11T17:53:19Z<p>WikiSysop: </p>
<hr />
<div>[[File:Algorithm.png|200px|thumb|right]]<br />
{{Nav-Bar|Topics##}}<br><br />
An '''Algorithm''' is defined as a set of precise steps and distinct states used to express the detailed structure of a program or the order of events that occurred in a system <ref>Cormen, Thomas H. et al. (2009). ''Introduction to Algorithms;'. MIT Press.</ref>.Algorithms are involved in many aspects of daily life and in complex computer science concepts. They often use repetition of operations to allow people and machines to execute tasks more efficiently by executing tasks faster and using fewer resources such as memory. On a basic level, an algorithm is a system working through different iterations of a process<ref>Lim, Brian (December 7, 2016). [e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/ "A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life)"] ''e27''.</ref>. They can help turn systematic and tedious tasks into fast, automated processes. Large companies particularly value robust algorithms because their infrastructure depends on efficiency to remain profitable on a massive scale<ref>Rastogi, Rajeev, and Kyuseok Shim (1999). "Scalable algorithms for mining large databases." ''Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining''. ACM.</ref>.<br />
<br />
=This is a major heading=<br />
== History ==<br />
The earliest known algorithms stem back to 1700 BC when the Egyptians created them for quick multiplication and division<ref>Stepanov, A. and Rose, D. (2014).<br />
''From Mathematics to Generic Programming: The First Algorithm''. [http://www.informit.com/articles/article.aspx?p=2264460 "Chapter 2"]. Retrieved April 28, 2019.</ref>. Since then, ancient Babylonians in 300 BC created algorithms to track farming and livestock using square roots. Following this, steady advancement gave birth to fundamental mathematical algorithm families like algebra, shaping the field of mathematics with its all-purpose formulas. The man often accredited as “The Father of Algebra,” Muhammad ibn Mūsa al-Khwarizmī, was also the one who gave English the word “algorithm” around 850 AD, as he wrote a book ''Al-Khwarizmi on the Hindu Art of Reckoning'', which in Latin translates to ''Algoritmi de Numero Indorum''. The English word [https://www.digit.in/features/science-and-technology/the-origin-of-algorithms-30045.html "algorithm"] was adopted from this title.<br />
<br />
A myriad of fundamental algorithms have been developed throughout history, ranging from pure mathematics to important computer science stalwarts, extending from ancient times up through the modern day. The computer revolution led to algorithms that can filter and personalize results based on the user.<br />
<br />
The [https://en.wikipedia.org/wiki/Timeline_of_algorithms Algorithm Timeline] outlines the advancements in the development of the algorithm as well as a number of the well-known algorithms developed from the Medieval Period until modern day.<br />
<br />
=== Computation ===<br />
Another cornerstone for algorithms comes from [https://en.wikipedia.org/wiki/Alan_Turing Alan Turing] and his contributions to cognitive and [https://en.wikipedia.org/wiki/Computer_science computer science]. Turing conceptualized the concept of cognition and designed ways to emulate human cognition with machines. This process turned the human thought process into mathematical algorithms and it led to the development of Turing Machines. It capitalized on these theoretical algorithms to perform unique functions and the development of computers. As their name suggests, computers utilized specific rules or algorithms to compute and it is these machines (or sometimes people)<ref>[crgis.ndc.nasa.gov/historic/Human_Computers "Human Computers"]. ''NASA Cultural Resources''. Retrieved April 28, 2019.</ref> that most often relate to the concept of algorithms that is used today. With the advent of mechanical computers, the computer science field paved the way for algorithms to run the world as they do now by calculating and controlling an immense quantity of facets of daily life. To this day, Turing machines are a main area of study in the theory of computation.<br />
<br />
=== Advancements In Algorithms ===<br />
In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity. Advanced algorithms, such as artificial intelligence is defined as utilizing machine learning capabilities.<ref>Anyoha, Rockwell (August 28, 2017). [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ "The History of Artificial Intelligence"]. ''Science in the News''. Harvard. Retrieved April 28, 2019.</ref> This level of algorithmic improvement provided the foundation for more technological advancement.<br />
[[File:machineLearning.png|500px]]<br />
<br />
The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.<br />
<br />
== Classifications ==<br />
There are many different classifications of algorithms, some are more well-suited for particular families of computational problems than others. In many cases, the algorithm one chooses to make for a given problem will have tradeoffs dealing with time complexity and memory usage.<br />
<br />
==== Recursive Algorithms ====<br />
<br />
A [https://en.wikipedia.org/wiki/Recursion_(computer_science) recursive algorithm] is an algorithm that calls itself with decreasing values in order to reach a pre-defined base case solution. The base case solution determines the values that are sent back up the recursive stack to determine the final outcome of the algorithm. It follows the principle of solving subproblems to solve the larger problem since once the base case solution is reached, the algorithm works upwards to fit the solution into the larger subproblem. The base case must be present, otherwise the recursive function will never stop calling itself, creating an infinite loop. Since recursion involves numerous function calls, it is one of the main sources of stack overflow. With recursive calls, programs have to save more stacks despite a lack of available space. Further, some recursive functions require additional computations even after the recursive call, adding to the consumption speed and memory. 'Tail Recursive' functions are an efficient solution to this, wherein recursive calls happen at the very end of the function, allowing only one stack to be saved throughout the function calls.<br />
<br />
Due to the recurring memory stack frames that are created with each call, recursive algorithms generally require more memory and computation power. However, they are still viewed as simplistic and succinct ways to write elaborate algorithms. <br />
<br />
==== Serial, Parallel or Distributed ====<br />
A [https://en.wikipedia.org/wiki/Sequential_algorithm serial algorithm] is an algorithm in which calculation is done in sequential manners on one core, it follows a defined order in solving a problem. Parallel algorithms utilizes the fact that modern computers have more than one cores, so that computations that are not interdependent could be calculated on separate cores. This is referred to as multi-threaded algorithm where each thread is a series of executing commands and they are inter-weaved to ensure correct output without deadlock. A deadlock occurs when there are interdependence between more than one thread so that none of the threads can continue until one of the other threads continues. Parallel algorithm is important that it allows more than one program to run at the same time by leveraging a computer's available resources otherwise would not have been possible with serial algorithm. Finally, distributed algorithm is similar to parallel algorithm in that it allows multiple programs to run at once with the exception that instead of leveraging multiple cores in a single computer it leverages multiple computers that communicates through a computer network. Similar to how parallel algorithm builds on serial algorithm with the added complexity of synchronizing threads to prevent deadlock and ensure correct outputs, distributed algorithm builds on parallel algorithm with the added complexity of managing communication latency and defining order since it is impossible to synchronize every computer's clock in a distributed system without significant compromises. A distributed algorithm provides extra reliability in that data are stored in more than one location so that one failure would not result in loss of data and by doing computation in multiple computer it can potentially have even faster computational speed.<br />
<br />
==== Deterministic vs Non-Deterministic ====<br />
Deterministic algorithms are those that solve problems with exact precision and ordering so a given input would produce the same output every time. Non-deterministic algorithms either have data races or utilize certain randomization, so the same input could have a myriad of outputs. Non-deterministic algorithms can be represented by flowcharts, programming languages, and machine language programs. However, they vary in that they are capable of using a multiple-valued function whose values are the positive integers less than or equal to itself. In addition, all points of termination are labeled as successes or failures. The terminology "non-deterministic" does not imply randomness, of rather a kind of free will<ref>"<br />
Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref><br />
<br />
==== Exact vs Approximation ====<br />
Some algorithms are implemented to solve for the exact solutions of a problem, whereas some problems are implemented for an approximation or heuristic. Approximation is important in which heuristics could provide an answer that is good enough such that the excessive computational time necessary to find the actual solution is not warranted since one would get minimal gains while expanding a great deal of resources. An example where an approximation is warranted is the traveling salesman's algorithm in which has computational complexity of O(n!), so an heuristic is necessary since some high values of n are not even possible to calculate.<br />
<br />
==== Brute Force or Exhaustive Search ====<br />
A brute force algorithm is the most "naive" approach one can take in attempting to solve a particular problem. A solution is reached by searching through every single possible outcome before arriving at an answer. In terms of complexity or Big-O notation, brute force algorithms typically represent the highest order complexity compared to other potential solutions for a given problem. While brute force algorithms may not be considered the most efficient option for solving computational problems, they do offer reliability as well as a guarantee that a solution to a given problem will eventually be found.<br />
<br />
An example of a brute force algorithm would be trying all combinations of a 4-digit passcode, in order to crack into a target's smartphone.<br />
<br />
==== Divide and Conquer ====<br />
A divide and conquer algorithm divides a problem into smaller sub-problems then conquer each smaller problems before merging them together to solve the original problem. In terms of efficiency and the Big-O notation, the Divide and Conquer fares better than Brute Force but is still relatively inefficient compared to other more complex algorithms. An example of divide and conquer is merge sort wherein a list is split into smaller sorted lists and then merged together to sort the original list.<br />
<br />
Examples of Divide and Conquer algorithms would be the sorting algorithm [https://en.wikipedia.org/wiki/Merge_sort Merge Sort], and the searching algorithm [https://en.wikipedia.org/wiki/Binary_search_algorithm Binary Search]<ref>[https://www.geeksforgeeks.org/divide-and-conquer-algorithm-introduction "Divide and Conquer Algorithms"]. ''Geeks for Geeks''. Retrieved April 28, 2019.</ref>.<br />
<br />
====Dynamic Programming====<br />
[https://.wikipedia.org/wiki/Dynamic_programming Dynamic programming] takes advantage of overlapping subproblems to more efficiently solve a larger computational problem. The algorithm first solves less complex subproblems and stores their solutions in memory. Then more complex problems will find these solutions using some method of lookup to find the solution and implement it in the more complex problem to find its own solution. The method of lookup enables solutions to be computed once and used multiple times. This method reduces the time complexity from exponential to polynomial.<br />
<br />
An example of a common problem that can be solved by Dynamic Programming is the [https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm 0-1 Knapsack Problem].<br />
<br />
==== Backtracking ====<br />
A backtracking algorithm is similar to brute force with the exception that as soon as it reaches a node where a solution could never be reached from said node on, it prunes all the subsequent node and backtracks to the closest node that has the possibility to be right. Pruning in this context means neglecting the failed branch as a potential solution branch in all further searches, reducing the scope of the possible solution set and eventually guiding the program to the right outcome.<br />
<br />
An example of some problems that can be solved by algorithms that take advantage of backtracking is solving [[Wikipedia:Sudoku|Sudoko]], or the [[Wikipedia:Eight_queens_puzzle|N-Queens Problem]].<br />
<br />
====Greedy Algorithm====<br />
An intuitive approach to design algorithms that don’t always yield the optimal solution. This approach required a collection of candidates, or options, in which the algorithm selects in order to satisfy a given predicate. Greedy algorithms can either favor the least element in the collection or the greatest in order to satisfy the predicate<ref>[https://brilliant.org/wiki/greedy-algorithm/ "Greedy Algorithms"]. ''Brilliant Math & Science Wiki''. Retrieved April 28, 2019.</ref>.<br />
<br />
An example of a greedy algorithm may take the form of selecting coins to make change in a transaction. The collection includes the official coins of the U.S. currency (25 cents, 10 cents, 5 cents, and 1 cent) and the predicate would be to make change of 11 cents. The greedy algorithm will select the greatest of our collection to approach 11, but not past it. The algorithm will first select a dime, then a penny, then end when 11 cents has been made.<br />
<br />
== Complexity and Big-O Notation ==<br />
[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]<br />
Measuring the efficiency of an algorithm is standardized by checking how well it grows with more inputs. Computer scientists calculate how much computational time increases with an increasing number of inputs. Since this form of measurement merely intends to see how well an algorithm grows, the constants are left out since with high inputs these constants are negligible anyways. Big-O notation specifically describes the worst-case scenario and measures the time or space the algorithm uses<ref name = 'Big-O'>[ Bell, Rob. [https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/ "A Beginner's Guide to Big O Notation"]. Retrieved April 28, 2019.</ref>. Big-O notation can be broken down into order of growth algorithms such as O(1), O(N), and O(log N), with each notation representing different orders of growth. The later, log algorithms -commonly referred to as logarithms, are bit more complex than the rest, log algorithms take a median from a data set and compare it to a target value, the algorithm continues to halve the data as long as the median is higher or lower than the target value<ref name='Big-O'></ref>. An algorithm with a higher Big-O is less efficient at large scales, for example in general a O(N) algorithm will run slower than a O(1) algorithm, and this difference will be more and more apparent, the larger the number of inputs.<br />
<br />
== Artificial Intelligence Algorithms ==<br />
=== Clustering ===<br />
Clustering is a [https://en.wikipedia.org/wiki/Machine_learning Machine Learning] technique in which, data is segregated into groups called clusters through an algorithm, given a set of data points. These clustering algorithms classify the data based on various criteria, but the fundamental premise is that data points with similarities will belong in the same group, that must be dissimilar to other groups. There are numerous clustering algorithms including K-Means clustering, Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering, EM (Expectation Maximization) Clustering, Agglomerative Hierarchical Clustering. <ref name="Clustering">Seif, George (February 5, 2018). [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 "The 5 Clustering Algorithms Data Scientists Need To Know"]. Retrieved April 28, 2019.</ref><br />
<br />
==== K-Means Clustering ====<br />
K-means clustering is the most widely known and used algorithm out of all the clustering algorithms. It involves pre-determining a target number - ''k'', which represents the number of centroids needed in the dataset. A centroid refers to the predicted center of the cluster. It then identifies the data points nearest to the center to form each cluster, while keeping ''k'' as small as possible. <ref>Garbade, Michael J. (September 12, 2018). [https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 "Understanding K-Means Clustering in Machine Learning"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> K-Means clustering is considered to be a fast algorithm, due to the minimal computations it requires. It has a Big-O complexity of O(''n''). <ref name="Clustering"></ref> <br />
<br />
==== Mean-Shift Clustering ====<br />
Mean-Shift Clustering, also known as Mode-Seeking, is an algorithm where datapoints are grouped into clusters, through the process of iteratively shifting all the points towards their mode. The mode of a dataset is defined as the most occurring value in that particular dataset, or in graphical terms, the point where the density of data points is the highest. Therefore, the algorithm moves, or "shifts" each point closer to its closest centroid, the direction of which is determined by the density of the nearby points. Therefore, each iteration of the program moves each point closer to where all the other points are, eventually forming a cluster center. The key difference between Mean-Shift and K-Means clustering is that K-Means requires the number ''k'' to be set beforehand, whereas the Mean-Shift algorithm creates clusters on the go without necessarily determining how many will be formed. <ref>[http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/ "Meanshift Algorithm for the Rest of Us (Python)"], May 14, 2016. Retrieved April 28, 2019.</ref> Usually, the Big-O complexity of such an algorithm is O(''Tn^2''), where ''T'' refers to the number of iterations in the algorithm. <ref>Thirumuruganathan, Saravanan (April 1, 2010). [https://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/ "Introduction To Mean Shift Algorithm"]. Retrieved April 28, 2019.</ref><br />
<br />
==== DBSCAN Clustering ====<br />
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that groups together nearby datapoints based on a measure of distance, often [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] and a minimal number of points. The algorithm also differentiates outliers if they are in low density areas. The algorithm requires two parameters - ''eps'' and ''minPoints''. The decision on what to set these parameters as depends from dataset to dataset, and requires a fundamental understanding of the context of the dataset being used. The ''eps'' should be picked based on the dataset distance and while generally small ''eps'' values are desirable, if the set value is too small there is a danger that a portion of the data will go unclustered. Conversely, if the value set is too large, too many of the points might get grouped into the same cluster. The minPoints parameter is usually derived from the parameter ''D'', following that minPoints ≥ ''D + 1'' where ''D'' measures the number of dimensions in the data. <ref>Salton do Prado, Kelvin (April 1, 2017). [https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80 "How DBSCAN works and why should we use it?"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> The average run-time complexity of a DBSCAN algorithm is O(''n log n'') whereas it's worst-case complexity can be O(''n^2'').<br />
<br />
==== EM Clustering ====<br />
Expectation Maximization or EM Clustering is similar to the K-Means clustering technique. The EM Clustering method takes forward the basic principles of K-Means Clustering in two primary ways:<br />
1) The EM Clustering method aims to calculate what datapoints belong in what cluster through one or more probability distributions, instead of trying to simply calculate and maximize the difference in mean points.<br />
2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.<br />
<br />
Essentially, the EM clustering method approximates the distribution of each point based on different probability distributions and at the end of it, each observation has a certain level of probability of belonging to a particular cluster. Ultimately, the resulting clusters are analyzed based on which clusters have the highest classification probabilities. <ref>[https://docs.rapidminer.com/latest/studio/operators/modeling/segmentation/expectation_maximization_clustering.html "Expectation Maximization Clustering"] ''RapidMiner Documentation''. Retrieved April 28, 2019.</ref><br />
<br />
==== Agglomerative Hierarchical Clustering ====<br />
Also known as AGNES (Agglomerative Nesting), the Agglomerative Hierarchical Clustering Technique also creates clusters based on similarity. To start, this method takes each object and treats it as if it were a cluster. It then merges each of these clusters in pairs, until one huge cluster consisting of all the individual clusters has been formed. The result is represented in the form of a tree, which is called a ''dendrogram''. The manner in which the algorithm works is called the "bottom-up" technique. Each data entry is considered an individual element or a leaf node. At each following stage, the element is joined with its closest or most-similar element to form a bigger element or parent node. The process is repeated over and over until the root node is formed, with all of the subsequent nodes under it.<br />
<br />
The opposite of this technique is through the "top-down" method, which is implemented in an algorithm called "Divisive Clustering". This method starts at the root node, and at each iteration nodes are split or "divided" into two separate nodes, based off the ranking of dissimilarity within the clusters. This is done until every node has been divided, leaving individual clusters or leaf nodes. <ref>[https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/ "HIERARCHICAL CLUSTERING IN R: THE ESSENTIALS/Agglomerative Hierarchical Clustering"]. ''DataNovia''. Retrieved April 28, 2019.</ref><br />
<br />
==== Deep Learning and Neural Networks ====<br />
Neural networks are a collection of algorithms that utilize many of the concepts mentioned above while taking their capabilities a step further through deep learning. On a high level, the purpose of a neural network is to interpret raw input data through a machine perception and return patterns in the data, through techniques above such as K-means clustering or Random Forests. To do so, a neural network requires datasets to train on and thus models its interpretations on the training sets in a machine learning process. Where neural networks differ is its ability to be “stacked” to engage in deep learning. Each process is held in nodes that can be likened to neurons in a human brain. When data is encountered, many separate computations occur that can be weighted to produce the desired output. How many “layers”, or the depth, of a neural network increases its capabilities and complexity multiplicatively. <ref> A Beginner's Guide to Neural Networks and Deep Learning. (n.d.). Retrieved April 27, 2019, from https://skymind.ai/wiki/neural-network </ref><br />
<br />
== Ethical Dilemmas ==<br />
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.<br />
<br />
=== Bias ===<br />
Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. <br />
<br />
====Joy Buolamwini and Facial Recognition====<br />
Joy Buolamwini, a graduate computer science student at MIT, experienced a case of this. The facial recognition software she was working on failed to detect her face, as she had a skin tone that had not been accounted for in the facial recognition algorithm. This is because the software had used machine learning with a dataset that was not diverse enough, and as a result, the algorithm failed to recognize her.<ref>Buolamwini, Joy. [www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/ "How I'm Fighting Bias in Algorithms"]. ''MIT Media Lab''. Retrieved April 28, 2019.</ref> Safiya Noble discusses instances of algorithmic search engines reinforcing racism in her book, "Algorithms of Oppression".<ref>Noble, Safiya. ''Algorithms of Oppression''.</ref> Bias like this occurs in countless algorithms, be it through insufficient machine learning data sets, or the algorithm developers own fault, among other reasons, and it has the potential to cause legitimate problems even outside the realm of ethics.<br />
<br />
====Bias in Criminalization====<br />
[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing COMPAS] is algorithm written to determine whether a criminal is likely to re-offend using information like age, gender, and previously committed crimes. Tests have found it to be more likely to incorrectly evaluate black people than white people because it has learned on historical criminal data, which has been influenced by our biased policing practices.<ref>Larson, Mattu; Kirchner, Angwin (May 23, 2016). [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm "How We Analyzed the COMPAS Recidivism Algorithm"]. ''Propublica''. Retrieved April 28, 2019.</ref> <br />
<br />
Jerry Kaplan is a research affiliate at Stanford University’s Center on Democracy, Development and the Rule of Law at the Freeman Spogli Institute for International Studies, where he teaches “Social and Economic Impact of Artificial Intelligence.” According to Kaplan, algorithmic bias can even influence whether or not a person is sent to jail. A 2016 study conducted by ProPublica indicated that software designed to predict the likelihood an arrestee will re-offend incorrectly flagged black defendants twice as frequently as white defendants in a decision-support system widely used by judges. Ideally, predictive systems should be wholly impartial and therefore be agnostic to skin color. However, surprisingly, the program cannot give black and white defendants who are otherwise identical the same risk score, and simultaneously match the actual recidivism rates for these two groups. This is because black defendants are re-arrested at higher rates that their white counterparts (52% versus 39%), at least in part due to racial profiling, inequities in enforcement, and harsher treatment of black people within the justice system. <ref>Kaplan, J. (December 17, 2018). [https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/ "Why your AI might be racist"]. ''Washington Post''. Retrieved April 28, 2019.</ref><br />
<br />
==== Job Applicants ====<br />
Many companies employ complex algorithms in order to review and sift the thousands of resumes they will receive each year. Sometimes these algorithms display a bias which can result in people with a specific racial background or gender being recommended over others. An example of this was an Amazon AI algorithm that preferred men over women in recommending people for an interview. The algorithm employed machine learning techniques and over time taught itself to prefer men over women due to a variety of factors <ref>Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women.” Reuters, Thomson Reuters, 9 Oct. 2018, af.reuters.com/.</ref>. A major problem facing machine learning algorithms is the unpredictability in their models and what they will begin to teach themselves. It was apparent Amazon engineers did not intend for their algorithm to be bias towards men but an error resulted in this happening. Additionally, this was not a quick fix as the algorithm had been in place for years and began to pick up this bias and after analysis after a period time, it was discovered.<br />
<br />
=== Privacy And Data Gathering ===<br />
The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref>Turilli, Matteo, and Luciano Floridi (2009). "The Ethics of Information Transparency." ''Ethics and Information Technology''. '''11'''(2): 105-112. doi:10.1007/s10676-009-9187-9.</ref> is an import point regarding these issues. In popular social media algorithms, user information is often probed without the knowledge of the individual, and this can lead to problems. It is often not transparent enough how these algorithms receive user data, resulting in often incorrect information which can affect both how a person is treated within social media, as well as how outside agents could view these individuals given false data. Algorithms can also often infringe on a user’s feelings of privacy, as data can be collected that a person would prefer to be private. Data brokers are in the business of collecting peoples in formation and selling it to anyone for a profit, like data brokers companies often have their own collection of data. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref>Griffin, Andrew (October 4, 2017) [http://www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html "Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth."] ''The Independent''. Retrieved April 28, 2019.</ref> The information leaked contained data relating to usernames, passwords, as well as dates of birth. Privacy and data gathering are common ethical dilemmas relating to algorithms and are often not considered thoroughly enough by algorithm’s users.<br />
<br />
===The Filter Bubble===<br />
[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]<br />
Algorithms can be used to filter results in order to prioritize items that the user might be interested in. On some platforms, like Amazon, people can find this filtering useful because of the useful shopping recommendations the algorithm can provide. However, in other scenarios, this algorithmic filtering can become a problem. For example, Facebook has an algorithm that re-orders the user's news feed. For a period of time, the technology company prioritized sponsored posts in their algorithm. This often prioritized news articles, but there was no certainty on whether these articles came from a reliable source, simply the fact that they were sponsored. Facebook also uses its technology to gather information about its users, like which political party they belong to. This combined with prioritizing news can create a Facebook feed filled with only one party's perspective. This phenomenon is called the filter bubble, which essentially creates a platform centered completely around its user's interests. <br />
<br />
Many, like Eli Pariser, have questioned the ethical implications of the filter bubble. Pariser believes that filter bubbles are a problem because they prevent users from seeing perspectives that might challenge their own. Even worse, Pariser emphasizes that this filter bubble is invisible, meaning that the people in it do not realize that they are in it. <ref>Pariser, Eli. (2012). ''The Filter Bubble''. Penguin Books.</ref> This creates a huge lack of awareness in the world, allowing people to stand by often uninformed opinions and creating separation, instead of collaboration, with users who have different beliefs. Because of the issues Pariser outlined, Facebook decided to change their algorithm in order to prioritize posts from friends and family, in hopes of eliminating the effects of the potential filter bubble.<br />
====Filter Bubble in Politics====<br />
Another issue that these Filter Bubbles create are echo chambers; Facebook, in particular, filters out [political] content that one might disagree with, or simply not enjoy <ref>Knight, Megan (November 30, 2018). [http://theconversation.com/explainer-how-facebook-has-become-the-worlds-largest-echo-chamber-91024 "Explainer: How Facebook Has Become the World's Largest Echo Chamber"]. ''The Conversation''. Retrieved April 28, 2019.</ref>. The more a user "likes" a particular type of content, similar content will continue to appear, and perhaps content that is even more extreme. This was clearly seen in the 2016 election when without realizing it, voters developed tunnel vision. Rarely did their Facebook comfort zones expose them to opposing views, and as a result they eventually became victims to their own biases and the biases embedded within the algorithms.<ref>El-Bermawy, Mostafa M. (June 3, 2017). [https://www.wired.com/2016/11/filter-bubble-destroying-democracy/ "Your Filter Bubble Is Destroying Democracy"]. ''Wired''. Retrieved April 28, 2019.</ref> Later studies produced visualizations to show how insular the country was at the time of the election on social media and the large divide between the two echo chambers with almost no ties to each other.<ref>MIS2: Misinformation and Misbehavior Mining on the Web - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 [accessed 23 Apr, 2019]</ref> <br />
<br />
[[File:Socal-med-polarizing.png|thumbnail|right|From [https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 Research Gate]: An example of algorithmic echo chambers that contribute to the polarization of political beliefs]]<br />
<br />
===Corrupt Personalization===<br />
Algorithms have the potential to become dangerous, with their most serious repercussions being the threat to democracy that is extensive personalization. Algorithms such as Facebook's are corrupt in the practice of "like recycling" that they partake in. In Christian Sandvig's article title ''Corrupt Personalization,'' he notes that Facebook has defined a "like" in two ways that the users do not realize. The first is that "anyone who clicks on a "like" button is considered to have "liked" all future content from that source," and the second is that "anyone who "likes" a comment on a shared link is considered to "like" wherever that link points to" <ref>Sandvig, Christian. “Corrupt Personalization.” Social Media Collective, 27 June 2014, socialmediacollective.org/2014/06/26/corrupt-personalization/</ref>. As a result, posts that you "like" wind up becoming ads on your friends' pages claiming that you like a certain item or thing. You are not able to see these posts and, because they do not appear on your news feed, you do not have the power to delete them. This becomes a threat to one's autonomy, for even if they wanted to delete this post, they can not. Furthermore, everyone is entitled to the ability to manage the public presentation of their own self-identity, and in this corrupt personalization, Facebook is giving users new aspects of their identity that may or may not be accurate. <br />
<br />
=== Agency And Accountability ===<br />
Algorithms make "decisions" based on the steps they were designed to follow and the input they received. This can often lead to algorithms as autonomous agents<ref>“Autonomous Agent.” Autonomous Agent - an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/computer-science/autonomous-agent.</ref>, taking decision making responsibilities out of the hands of real people. Useful in terms of efficiency, these autonomous agents are capable of making decisions in a greater frequency than humans. Efficiency is what materializes the baseline for algorithm use in the first place.<br />
<br />
From an ethical standpoint, this type of agency raises many complications, specifically regarding accountability. It is no secret that many aspects of life are run by algorithms. Even events like applying to jobs are often drastically effected by these processes. Information like age, race, status, along with other qualifications, are all fed to algorithms, which then take agency and decide who moves further along in the hiring process and who is left behind. Disregarding inherent biases in this specific scenario, this process still serves to reduce the input of real humans and decrease the number of decisions that they have to make, and what is left over is the fact that autonomous agents are making systematic decisions that have extraordinary impact on people's lives. While the results of the previous example may only culminate to the occasional disregard of a qualified applicant or resentful feelings, this same principle can be much more influential.<br />
<br />
====The Trolley Problem in Practice====<br />
Consider autonomous vehicles, or self-driving cars, for instance. These are highly advanced algorithms that are programmed to make split second decisions with the greatest possible accuracy. In the case of the well-known "Trolley Problem"<ref>Roff, Heather M. “The Folly of Trolleys: Ethical Challenges and Autonomous Vehicles.” Brookings, Brookings, 17 Dec. 2018, www.brookings.edu/research/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/.</ref>, these agents are forced to make a decision jeopardizing one party or another. This decision can easily conclude in the injury or even death of individuals, all at the discretion of a mere program. <br />
<br />
The issue of accountability is then raised, in a situation such as this, because in the eyes of the law, society, and ethical observers, there must be someone held responsible. Attempting to prosecute a program would not be feasible in a legal situation, due to not being able to have a physical representation of the program, like you would a person. However, there are those such as Frances Grodzinsky and Kirsten Martin <ref>Martin, Kirsten. “Ethical Implications and Accountability of Algorithms.” SpringerLink, Springer Netherlands, 7 June 2018, link.springer.com/article/10.1007/s10551-018-3921-3.</ref>, who believe that the designers of an artificial agent, should be responsible for the actions of the program. <ref name="Grodzinsky"> "The ethics of designing artificial agents" by Frances S. Grodzinsky et al, Springer, 2008.</ref> Others contend this point by saying that the blame should be attributed to the users or persons directly involved in the situation. <br />
<br />
These complications will continue to arise, especially as algorithms continue to make autonomous decisions at grander scales and rates. Determining responsibility for the decisions these agents make will continue to be a vexing process, and will no doubt shape in some form many of the advanced algorithms that will be developed in the coming years.<br />
<br />
=== Intentions and Consequences ===<br />
The ethical consequences that are common in algorithm implementations can be either deliberate or unintentional. Instances where an algorithm's intent and outcome differs is noted below. <br />
<br />
==== YouTube Radicalization ====<br />
Scholar and technosociologist Zeynep Tufekci has claimed that "[[YouTube]] may be one of the most powerful radicalizing instruments of the 21st century."<ref name="YouTube Radicalization">[https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html] Tufekci, Zeynep. “YouTube, the Great Radicalizer.” The New York Times, The New York Times, 10 Mar. 2018, www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html.</ref> As YouTube algorithms aim maximize the amount of time that viewers spend watching, it inevitably discovered that the best way to do this was to show videos that slowly "up the stakes" of the subject being watched - from jogging to ultramarathons, from vegetarianism to veganism, from Trump speeches to white supremacist rants.<ref name="YouTube Radicalization" /> Thus, while the intention of Youtube is to keep viewers watching (and bring in advertisement money), they have unintentionally created a site that shows viewers more and more extreme content - contributing to radicalization. Such activity circles back to and produce filters and echo chambers.<br />
<br />
==== Facebook Advertising ====<br />
By taking a closer look at Facebook's algorithm that serves up ads to its users, gender and racial bias is obviously prominent. Using demographic and racial background as factors, Facebook's decides which ads are served up to its users. A team from Northeastern tested to see the algorithm bias in action and by running identical ads with slight tweaks to budget, images, and headings, the ads reached vastly different audiences. Results such as minorities receiving a higher percentage of low-cost housing ads, and women receiving more ads for secretary and nursing jobs <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Although the intent of Facebook may be to reach people that these ads are intended for, the companies that are signing up to advertise with Facebook have stated they did not anticipate this type of filtering when paying for their services. Additionally, although Facebook may believe it is win-win for everyone since advertisers will be getting more interactions with certain audiences targeted, and people will be happy to see ads more relatable to them shown, it is incredibly discriminatory to target people based on factors that are uncontrollable. Facebook needs to adjust its targeted advertising practices by removing racial and gender factors in their algorithms in order to prevent perpetuating stereotypes and placing people in certain boxes by race and gender. This type of algorithm goes against many ethical principles and is important that powerful technology companies are not setting poor examples for others.<br />
<br />
==See also==<br />
{{resource|<br />
*[[Bias in Information]]<br />
*[[Artificial Agents]]<br />
*[[Value Sensitive Design]]<br />
*[[Artificial Intelligence and Technology]]<br />
}}<br />
<br />
== References ==<br />
<references/><br />
[[Category:2019New]]<br />
[[Category:Concepts]]<br />
[[Category:BlueStar2019]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Algorithms&diff=91439Algorithms2021-02-11T17:52:23Z<p>WikiSysop: </p>
<hr />
<div>[[File:Algorithm.png|200px|thumb|right]]<br />
{{Nav-Bar|Topics##}}<br><br />
An '''Algorithm''' is defined as a set of precise steps and distinct states used to express the detailed structure of a program or the order of events that occurred in a system <ref>Cormen, Thomas H. et al. (2009). ''Introduction to Algorithms;'. MIT Press.</ref>.Algorithms are involved in many aspects of daily life and in complex computer science concepts. They often use repetition of operations to allow people and machines to execute tasks more efficiently by executing tasks faster and using fewer resources such as memory. On a basic level, an algorithm is a system working through different iterations of a process<ref>Lim, Brian (December 7, 2016). [e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/ "A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life)"] ''e27''.</ref>. They can help turn systematic and tedious tasks into fast, automated processes. Large companies particularly value robust algorithms because their infrastructure depends on efficiency to remain profitable on a massive scale<ref>Rastogi, Rajeev, and Kyuseok Shim (1999). "Scalable algorithms for mining large databases." ''Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining''. ACM.</ref>.<br />
<br />
Processes like cooking a meal or reading a manual to assemble a new piece of furniture are examples of algorithms in everyday life<ref>T.C. (August 29, 2017). [https://www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms "What Are Algorithms?"] ''The Economist''. Retrieved April 28, 2019.</ref>. Algorithms are grounded in logic. The increase in their logical complexity via advancements in technology and human effort have provided the foundations for technological concepts such as artificial intelligence and machine learning<ref>McClelland, Calum. (December 4, 2017). [https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991 "The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning"]. ''Medium''. Retrieved April 28, 2019.</ref>. The influence of algorithms is pervasive and in computer science so it leads to an increase in ethical concerns in the areas of bias, privacy, and accountability.<br />
<br />
=This is a major heading=<br />
== History ==<br />
The earliest known algorithms stem back to 1700 BC when the Egyptians created them for quick multiplication and division<ref>Stepanov, A. and Rose, D. (2014).<br />
''From Mathematics to Generic Programming: The First Algorithm''. [http://www.informit.com/articles/article.aspx?p=2264460 "Chapter 2"]. Retrieved April 28, 2019.</ref>. Since then, ancient Babylonians in 300 BC created algorithms to track farming and livestock using square roots. Following this, steady advancement gave birth to fundamental mathematical algorithm families like algebra, shaping the field of mathematics with its all-purpose formulas. The man often accredited as “The Father of Algebra,” Muhammad ibn Mūsa al-Khwarizmī, was also the one who gave English the word “algorithm” around 850 AD, as he wrote a book ''Al-Khwarizmi on the Hindu Art of Reckoning'', which in Latin translates to ''Algoritmi de Numero Indorum''. The English word [https://www.digit.in/features/science-and-technology/the-origin-of-algorithms-30045.html "algorithm"] was adopted from this title.<br />
<br />
A myriad of fundamental algorithms have been developed throughout history, ranging from pure mathematics to important computer science stalwarts, extending from ancient times up through the modern day. The computer revolution led to algorithms that can filter and personalize results based on the user.<br />
<br />
The [https://en.wikipedia.org/wiki/Timeline_of_algorithms Algorithm Timeline] outlines the advancements in the development of the algorithm as well as a number of the well-known algorithms developed from the Medieval Period until modern day.<br />
<br />
=== Computation ===<br />
Another cornerstone for algorithms comes from [https://en.wikipedia.org/wiki/Alan_Turing Alan Turing] and his contributions to cognitive and [https://en.wikipedia.org/wiki/Computer_science computer science]. Turing conceptualized the concept of cognition and designed ways to emulate human cognition with machines. This process turned the human thought process into mathematical algorithms and it led to the development of Turing Machines. It capitalized on these theoretical algorithms to perform unique functions and the development of computers. As their name suggests, computers utilized specific rules or algorithms to compute and it is these machines (or sometimes people)<ref>[crgis.ndc.nasa.gov/historic/Human_Computers "Human Computers"]. ''NASA Cultural Resources''. Retrieved April 28, 2019.</ref> that most often relate to the concept of algorithms that is used today. With the advent of mechanical computers, the computer science field paved the way for algorithms to run the world as they do now by calculating and controlling an immense quantity of facets of daily life. To this day, Turing machines are a main area of study in the theory of computation.<br />
<br />
=== Advancements In Algorithms ===<br />
In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity. Advanced algorithms, such as artificial intelligence is defined as utilizing machine learning capabilities.<ref>Anyoha, Rockwell (August 28, 2017). [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ "The History of Artificial Intelligence"]. ''Science in the News''. Harvard. Retrieved April 28, 2019.</ref> This level of algorithmic improvement provided the foundation for more technological advancement.<br />
[[File:machineLearning.png|500px]]<br />
<br />
The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.<br />
<br />
== Classifications ==<br />
There are many different classifications of algorithms, some are more well-suited for particular families of computational problems than others. In many cases, the algorithm one chooses to make for a given problem will have tradeoffs dealing with time complexity and memory usage.<br />
<br />
==== Recursive Algorithms ====<br />
<br />
A [https://en.wikipedia.org/wiki/Recursion_(computer_science) recursive algorithm] is an algorithm that calls itself with decreasing values in order to reach a pre-defined base case solution. The base case solution determines the values that are sent back up the recursive stack to determine the final outcome of the algorithm. It follows the principle of solving subproblems to solve the larger problem since once the base case solution is reached, the algorithm works upwards to fit the solution into the larger subproblem. The base case must be present, otherwise the recursive function will never stop calling itself, creating an infinite loop. Since recursion involves numerous function calls, it is one of the main sources of stack overflow. With recursive calls, programs have to save more stacks despite a lack of available space. Further, some recursive functions require additional computations even after the recursive call, adding to the consumption speed and memory. 'Tail Recursive' functions are an efficient solution to this, wherein recursive calls happen at the very end of the function, allowing only one stack to be saved throughout the function calls.<br />
<br />
Due to the recurring memory stack frames that are created with each call, recursive algorithms generally require more memory and computation power. However, they are still viewed as simplistic and succinct ways to write elaborate algorithms. <br />
<br />
==== Serial, Parallel or Distributed ====<br />
A [https://en.wikipedia.org/wiki/Sequential_algorithm serial algorithm] is an algorithm in which calculation is done in sequential manners on one core, it follows a defined order in solving a problem. Parallel algorithms utilizes the fact that modern computers have more than one cores, so that computations that are not interdependent could be calculated on separate cores. This is referred to as multi-threaded algorithm where each thread is a series of executing commands and they are inter-weaved to ensure correct output without deadlock. A deadlock occurs when there are interdependence between more than one thread so that none of the threads can continue until one of the other threads continues. Parallel algorithm is important that it allows more than one program to run at the same time by leveraging a computer's available resources otherwise would not have been possible with serial algorithm. Finally, distributed algorithm is similar to parallel algorithm in that it allows multiple programs to run at once with the exception that instead of leveraging multiple cores in a single computer it leverages multiple computers that communicates through a computer network. Similar to how parallel algorithm builds on serial algorithm with the added complexity of synchronizing threads to prevent deadlock and ensure correct outputs, distributed algorithm builds on parallel algorithm with the added complexity of managing communication latency and defining order since it is impossible to synchronize every computer's clock in a distributed system without significant compromises. A distributed algorithm provides extra reliability in that data are stored in more than one location so that one failure would not result in loss of data and by doing computation in multiple computer it can potentially have even faster computational speed.<br />
<br />
==== Deterministic vs Non-Deterministic ====<br />
Deterministic algorithms are those that solve problems with exact precision and ordering so a given input would produce the same output every time. Non-deterministic algorithms either have data races or utilize certain randomization, so the same input could have a myriad of outputs. Non-deterministic algorithms can be represented by flowcharts, programming languages, and machine language programs. However, they vary in that they are capable of using a multiple-valued function whose values are the positive integers less than or equal to itself. In addition, all points of termination are labeled as successes or failures. The terminology "non-deterministic" does not imply randomness, of rather a kind of free will<ref>"<br />
Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref><br />
<br />
==== Exact vs Approximation ====<br />
Some algorithms are implemented to solve for the exact solutions of a problem, whereas some problems are implemented for an approximation or heuristic. Approximation is important in which heuristics could provide an answer that is good enough such that the excessive computational time necessary to find the actual solution is not warranted since one would get minimal gains while expanding a great deal of resources. An example where an approximation is warranted is the traveling salesman's algorithm in which has computational complexity of O(n!), so an heuristic is necessary since some high values of n are not even possible to calculate.<br />
<br />
==== Brute Force or Exhaustive Search ====<br />
A brute force algorithm is the most "naive" approach one can take in attempting to solve a particular problem. A solution is reached by searching through every single possible outcome before arriving at an answer. In terms of complexity or Big-O notation, brute force algorithms typically represent the highest order complexity compared to other potential solutions for a given problem. While brute force algorithms may not be considered the most efficient option for solving computational problems, they do offer reliability as well as a guarantee that a solution to a given problem will eventually be found.<br />
<br />
An example of a brute force algorithm would be trying all combinations of a 4-digit passcode, in order to crack into a target's smartphone.<br />
<br />
==== Divide and Conquer ====<br />
A divide and conquer algorithm divides a problem into smaller sub-problems then conquer each smaller problems before merging them together to solve the original problem. In terms of efficiency and the Big-O notation, the Divide and Conquer fares better than Brute Force but is still relatively inefficient compared to other more complex algorithms. An example of divide and conquer is merge sort wherein a list is split into smaller sorted lists and then merged together to sort the original list.<br />
<br />
Examples of Divide and Conquer algorithms would be the sorting algorithm [https://en.wikipedia.org/wiki/Merge_sort Merge Sort], and the searching algorithm [https://en.wikipedia.org/wiki/Binary_search_algorithm Binary Search]<ref>[https://www.geeksforgeeks.org/divide-and-conquer-algorithm-introduction "Divide and Conquer Algorithms"]. ''Geeks for Geeks''. Retrieved April 28, 2019.</ref>.<br />
<br />
====Dynamic Programming====<br />
[https://.wikipedia.org/wiki/Dynamic_programming Dynamic programming] takes advantage of overlapping subproblems to more efficiently solve a larger computational problem. The algorithm first solves less complex subproblems and stores their solutions in memory. Then more complex problems will find these solutions using some method of lookup to find the solution and implement it in the more complex problem to find its own solution. The method of lookup enables solutions to be computed once and used multiple times. This method reduces the time complexity from exponential to polynomial.<br />
<br />
An example of a common problem that can be solved by Dynamic Programming is the [https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm 0-1 Knapsack Problem].<br />
<br />
==== Backtracking ====<br />
A backtracking algorithm is similar to brute force with the exception that as soon as it reaches a node where a solution could never be reached from said node on, it prunes all the subsequent node and backtracks to the closest node that has the possibility to be right. Pruning in this context means neglecting the failed branch as a potential solution branch in all further searches, reducing the scope of the possible solution set and eventually guiding the program to the right outcome.<br />
<br />
An example of some problems that can be solved by algorithms that take advantage of backtracking is solving [[Wikipedia:Sudoku|Sudoko]], or the [[Wikipedia:Eight_queens_puzzle|N-Queens Problem]].<br />
<br />
====Greedy Algorithm====<br />
An intuitive approach to design algorithms that don’t always yield the optimal solution. This approach required a collection of candidates, or options, in which the algorithm selects in order to satisfy a given predicate. Greedy algorithms can either favor the least element in the collection or the greatest in order to satisfy the predicate<ref>[https://brilliant.org/wiki/greedy-algorithm/ "Greedy Algorithms"]. ''Brilliant Math & Science Wiki''. Retrieved April 28, 2019.</ref>.<br />
<br />
An example of a greedy algorithm may take the form of selecting coins to make change in a transaction. The collection includes the official coins of the U.S. currency (25 cents, 10 cents, 5 cents, and 1 cent) and the predicate would be to make change of 11 cents. The greedy algorithm will select the greatest of our collection to approach 11, but not past it. The algorithm will first select a dime, then a penny, then end when 11 cents has been made.<br />
<br />
== Complexity and Big-O Notation ==<br />
[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]<br />
Measuring the efficiency of an algorithm is standardized by checking how well it grows with more inputs. Computer scientists calculate how much computational time increases with an increasing number of inputs. Since this form of measurement merely intends to see how well an algorithm grows, the constants are left out since with high inputs these constants are negligible anyways. Big-O notation specifically describes the worst-case scenario and measures the time or space the algorithm uses<ref name = 'Big-O'>[ Bell, Rob. [https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/ "A Beginner's Guide to Big O Notation"]. Retrieved April 28, 2019.</ref>. Big-O notation can be broken down into order of growth algorithms such as O(1), O(N), and O(log N), with each notation representing different orders of growth. The later, log algorithms -commonly referred to as logarithms, are bit more complex than the rest, log algorithms take a median from a data set and compare it to a target value, the algorithm continues to halve the data as long as the median is higher or lower than the target value<ref name='Big-O'></ref>. An algorithm with a higher Big-O is less efficient at large scales, for example in general a O(N) algorithm will run slower than a O(1) algorithm, and this difference will be more and more apparent, the larger the number of inputs.<br />
<br />
== Artificial Intelligence Algorithms ==<br />
=== Clustering ===<br />
Clustering is a [https://en.wikipedia.org/wiki/Machine_learning Machine Learning] technique in which, data is segregated into groups called clusters through an algorithm, given a set of data points. These clustering algorithms classify the data based on various criteria, but the fundamental premise is that data points with similarities will belong in the same group, that must be dissimilar to other groups. There are numerous clustering algorithms including K-Means clustering, Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering, EM (Expectation Maximization) Clustering, Agglomerative Hierarchical Clustering. <ref name="Clustering">Seif, George (February 5, 2018). [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 "The 5 Clustering Algorithms Data Scientists Need To Know"]. Retrieved April 28, 2019.</ref><br />
<br />
==== K-Means Clustering ====<br />
K-means clustering is the most widely known and used algorithm out of all the clustering algorithms. It involves pre-determining a target number - ''k'', which represents the number of centroids needed in the dataset. A centroid refers to the predicted center of the cluster. It then identifies the data points nearest to the center to form each cluster, while keeping ''k'' as small as possible. <ref>Garbade, Michael J. (September 12, 2018). [https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 "Understanding K-Means Clustering in Machine Learning"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> K-Means clustering is considered to be a fast algorithm, due to the minimal computations it requires. It has a Big-O complexity of O(''n''). <ref name="Clustering"></ref> <br />
<br />
==== Mean-Shift Clustering ====<br />
Mean-Shift Clustering, also known as Mode-Seeking, is an algorithm where datapoints are grouped into clusters, through the process of iteratively shifting all the points towards their mode. The mode of a dataset is defined as the most occurring value in that particular dataset, or in graphical terms, the point where the density of data points is the highest. Therefore, the algorithm moves, or "shifts" each point closer to its closest centroid, the direction of which is determined by the density of the nearby points. Therefore, each iteration of the program moves each point closer to where all the other points are, eventually forming a cluster center. The key difference between Mean-Shift and K-Means clustering is that K-Means requires the number ''k'' to be set beforehand, whereas the Mean-Shift algorithm creates clusters on the go without necessarily determining how many will be formed. <ref>[http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/ "Meanshift Algorithm for the Rest of Us (Python)"], May 14, 2016. Retrieved April 28, 2019.</ref> Usually, the Big-O complexity of such an algorithm is O(''Tn^2''), where ''T'' refers to the number of iterations in the algorithm. <ref>Thirumuruganathan, Saravanan (April 1, 2010). [https://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/ "Introduction To Mean Shift Algorithm"]. Retrieved April 28, 2019.</ref><br />
<br />
==== DBSCAN Clustering ====<br />
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that groups together nearby datapoints based on a measure of distance, often [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] and a minimal number of points. The algorithm also differentiates outliers if they are in low density areas. The algorithm requires two parameters - ''eps'' and ''minPoints''. The decision on what to set these parameters as depends from dataset to dataset, and requires a fundamental understanding of the context of the dataset being used. The ''eps'' should be picked based on the dataset distance and while generally small ''eps'' values are desirable, if the set value is too small there is a danger that a portion of the data will go unclustered. Conversely, if the value set is too large, too many of the points might get grouped into the same cluster. The minPoints parameter is usually derived from the parameter ''D'', following that minPoints ≥ ''D + 1'' where ''D'' measures the number of dimensions in the data. <ref>Salton do Prado, Kelvin (April 1, 2017). [https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80 "How DBSCAN works and why should we use it?"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> The average run-time complexity of a DBSCAN algorithm is O(''n log n'') whereas it's worst-case complexity can be O(''n^2'').<br />
<br />
==== EM Clustering ====<br />
Expectation Maximization or EM Clustering is similar to the K-Means clustering technique. The EM Clustering method takes forward the basic principles of K-Means Clustering in two primary ways:<br />
1) The EM Clustering method aims to calculate what datapoints belong in what cluster through one or more probability distributions, instead of trying to simply calculate and maximize the difference in mean points.<br />
2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.<br />
<br />
Essentially, the EM clustering method approximates the distribution of each point based on different probability distributions and at the end of it, each observation has a certain level of probability of belonging to a particular cluster. Ultimately, the resulting clusters are analyzed based on which clusters have the highest classification probabilities. <ref>[https://docs.rapidminer.com/latest/studio/operators/modeling/segmentation/expectation_maximization_clustering.html "Expectation Maximization Clustering"] ''RapidMiner Documentation''. Retrieved April 28, 2019.</ref><br />
<br />
==== Agglomerative Hierarchical Clustering ====<br />
Also known as AGNES (Agglomerative Nesting), the Agglomerative Hierarchical Clustering Technique also creates clusters based on similarity. To start, this method takes each object and treats it as if it were a cluster. It then merges each of these clusters in pairs, until one huge cluster consisting of all the individual clusters has been formed. The result is represented in the form of a tree, which is called a ''dendrogram''. The manner in which the algorithm works is called the "bottom-up" technique. Each data entry is considered an individual element or a leaf node. At each following stage, the element is joined with its closest or most-similar element to form a bigger element or parent node. The process is repeated over and over until the root node is formed, with all of the subsequent nodes under it.<br />
<br />
The opposite of this technique is through the "top-down" method, which is implemented in an algorithm called "Divisive Clustering". This method starts at the root node, and at each iteration nodes are split or "divided" into two separate nodes, based off the ranking of dissimilarity within the clusters. This is done until every node has been divided, leaving individual clusters or leaf nodes. <ref>[https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/ "HIERARCHICAL CLUSTERING IN R: THE ESSENTIALS/Agglomerative Hierarchical Clustering"]. ''DataNovia''. Retrieved April 28, 2019.</ref><br />
<br />
==== Deep Learning and Neural Networks ====<br />
Neural networks are a collection of algorithms that utilize many of the concepts mentioned above while taking their capabilities a step further through deep learning. On a high level, the purpose of a neural network is to interpret raw input data through a machine perception and return patterns in the data, through techniques above such as K-means clustering or Random Forests. To do so, a neural network requires datasets to train on and thus models its interpretations on the training sets in a machine learning process. Where neural networks differ is its ability to be “stacked” to engage in deep learning. Each process is held in nodes that can be likened to neurons in a human brain. When data is encountered, many separate computations occur that can be weighted to produce the desired output. How many “layers”, or the depth, of a neural network increases its capabilities and complexity multiplicatively. <ref> A Beginner's Guide to Neural Networks and Deep Learning. (n.d.). Retrieved April 27, 2019, from https://skymind.ai/wiki/neural-network </ref><br />
<br />
== Ethical Dilemmas ==<br />
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.<br />
<br />
=== Bias ===<br />
Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. <br />
<br />
====Joy Buolamwini and Facial Recognition====<br />
Joy Buolamwini, a graduate computer science student at MIT, experienced a case of this. The facial recognition software she was working on failed to detect her face, as she had a skin tone that had not been accounted for in the facial recognition algorithm. This is because the software had used machine learning with a dataset that was not diverse enough, and as a result, the algorithm failed to recognize her.<ref>Buolamwini, Joy. [www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/ "How I'm Fighting Bias in Algorithms"]. ''MIT Media Lab''. Retrieved April 28, 2019.</ref> Safiya Noble discusses instances of algorithmic search engines reinforcing racism in her book, "Algorithms of Oppression".<ref>Noble, Safiya. ''Algorithms of Oppression''.</ref> Bias like this occurs in countless algorithms, be it through insufficient machine learning data sets, or the algorithm developers own fault, among other reasons, and it has the potential to cause legitimate problems even outside the realm of ethics.<br />
<br />
====Bias in Criminalization====<br />
[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing COMPAS] is algorithm written to determine whether a criminal is likely to re-offend using information like age, gender, and previously committed crimes. Tests have found it to be more likely to incorrectly evaluate black people than white people because it has learned on historical criminal data, which has been influenced by our biased policing practices.<ref>Larson, Mattu; Kirchner, Angwin (May 23, 2016). [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm "How We Analyzed the COMPAS Recidivism Algorithm"]. ''Propublica''. Retrieved April 28, 2019.</ref> <br />
<br />
Jerry Kaplan is a research affiliate at Stanford University’s Center on Democracy, Development and the Rule of Law at the Freeman Spogli Institute for International Studies, where he teaches “Social and Economic Impact of Artificial Intelligence.” According to Kaplan, algorithmic bias can even influence whether or not a person is sent to jail. A 2016 study conducted by ProPublica indicated that software designed to predict the likelihood an arrestee will re-offend incorrectly flagged black defendants twice as frequently as white defendants in a decision-support system widely used by judges. Ideally, predictive systems should be wholly impartial and therefore be agnostic to skin color. However, surprisingly, the program cannot give black and white defendants who are otherwise identical the same risk score, and simultaneously match the actual recidivism rates for these two groups. This is because black defendants are re-arrested at higher rates that their white counterparts (52% versus 39%), at least in part due to racial profiling, inequities in enforcement, and harsher treatment of black people within the justice system. <ref>Kaplan, J. (December 17, 2018). [https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/ "Why your AI might be racist"]. ''Washington Post''. Retrieved April 28, 2019.</ref><br />
<br />
==== Job Applicants ====<br />
Many companies employ complex algorithms in order to review and sift the thousands of resumes they will receive each year. Sometimes these algorithms display a bias which can result in people with a specific racial background or gender being recommended over others. An example of this was an Amazon AI algorithm that preferred men over women in recommending people for an interview. The algorithm employed machine learning techniques and over time taught itself to prefer men over women due to a variety of factors <ref>Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women.” Reuters, Thomson Reuters, 9 Oct. 2018, af.reuters.com/.</ref>. A major problem facing machine learning algorithms is the unpredictability in their models and what they will begin to teach themselves. It was apparent Amazon engineers did not intend for their algorithm to be bias towards men but an error resulted in this happening. Additionally, this was not a quick fix as the algorithm had been in place for years and began to pick up this bias and after analysis after a period time, it was discovered.<br />
<br />
=== Privacy And Data Gathering ===<br />
The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref>Turilli, Matteo, and Luciano Floridi (2009). "The Ethics of Information Transparency." ''Ethics and Information Technology''. '''11'''(2): 105-112. doi:10.1007/s10676-009-9187-9.</ref> is an import point regarding these issues. In popular social media algorithms, user information is often probed without the knowledge of the individual, and this can lead to problems. It is often not transparent enough how these algorithms receive user data, resulting in often incorrect information which can affect both how a person is treated within social media, as well as how outside agents could view these individuals given false data. Algorithms can also often infringe on a user’s feelings of privacy, as data can be collected that a person would prefer to be private. Data brokers are in the business of collecting peoples in formation and selling it to anyone for a profit, like data brokers companies often have their own collection of data. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref>Griffin, Andrew (October 4, 2017) [http://www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html "Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth."] ''The Independent''. Retrieved April 28, 2019.</ref> The information leaked contained data relating to usernames, passwords, as well as dates of birth. Privacy and data gathering are common ethical dilemmas relating to algorithms and are often not considered thoroughly enough by algorithm’s users.<br />
<br />
===The Filter Bubble===<br />
[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]<br />
Algorithms can be used to filter results in order to prioritize items that the user might be interested in. On some platforms, like Amazon, people can find this filtering useful because of the useful shopping recommendations the algorithm can provide. However, in other scenarios, this algorithmic filtering can become a problem. For example, Facebook has an algorithm that re-orders the user's news feed. For a period of time, the technology company prioritized sponsored posts in their algorithm. This often prioritized news articles, but there was no certainty on whether these articles came from a reliable source, simply the fact that they were sponsored. Facebook also uses its technology to gather information about its users, like which political party they belong to. This combined with prioritizing news can create a Facebook feed filled with only one party's perspective. This phenomenon is called the filter bubble, which essentially creates a platform centered completely around its user's interests. <br />
<br />
Many, like Eli Pariser, have questioned the ethical implications of the filter bubble. Pariser believes that filter bubbles are a problem because they prevent users from seeing perspectives that might challenge their own. Even worse, Pariser emphasizes that this filter bubble is invisible, meaning that the people in it do not realize that they are in it. <ref>Pariser, Eli. (2012). ''The Filter Bubble''. Penguin Books.</ref> This creates a huge lack of awareness in the world, allowing people to stand by often uninformed opinions and creating separation, instead of collaboration, with users who have different beliefs. Because of the issues Pariser outlined, Facebook decided to change their algorithm in order to prioritize posts from friends and family, in hopes of eliminating the effects of the potential filter bubble.<br />
====Filter Bubble in Politics====<br />
Another issue that these Filter Bubbles create are echo chambers; Facebook, in particular, filters out [political] content that one might disagree with, or simply not enjoy <ref>Knight, Megan (November 30, 2018). [http://theconversation.com/explainer-how-facebook-has-become-the-worlds-largest-echo-chamber-91024 "Explainer: How Facebook Has Become the World's Largest Echo Chamber"]. ''The Conversation''. Retrieved April 28, 2019.</ref>. The more a user "likes" a particular type of content, similar content will continue to appear, and perhaps content that is even more extreme. This was clearly seen in the 2016 election when without realizing it, voters developed tunnel vision. Rarely did their Facebook comfort zones expose them to opposing views, and as a result they eventually became victims to their own biases and the biases embedded within the algorithms.<ref>El-Bermawy, Mostafa M. (June 3, 2017). [https://www.wired.com/2016/11/filter-bubble-destroying-democracy/ "Your Filter Bubble Is Destroying Democracy"]. ''Wired''. Retrieved April 28, 2019.</ref> Later studies produced visualizations to show how insular the country was at the time of the election on social media and the large divide between the two echo chambers with almost no ties to each other.<ref>MIS2: Misinformation and Misbehavior Mining on the Web - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 [accessed 23 Apr, 2019]</ref> <br />
<br />
[[File:Socal-med-polarizing.png|thumbnail|right|From [https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 Research Gate]: An example of algorithmic echo chambers that contribute to the polarization of political beliefs]]<br />
<br />
===Corrupt Personalization===<br />
Algorithms have the potential to become dangerous, with their most serious repercussions being the threat to democracy that is extensive personalization. Algorithms such as Facebook's are corrupt in the practice of "like recycling" that they partake in. In Christian Sandvig's article title ''Corrupt Personalization,'' he notes that Facebook has defined a "like" in two ways that the users do not realize. The first is that "anyone who clicks on a "like" button is considered to have "liked" all future content from that source," and the second is that "anyone who "likes" a comment on a shared link is considered to "like" wherever that link points to" <ref>Sandvig, Christian. “Corrupt Personalization.” Social Media Collective, 27 June 2014, socialmediacollective.org/2014/06/26/corrupt-personalization/</ref>. As a result, posts that you "like" wind up becoming ads on your friends' pages claiming that you like a certain item or thing. You are not able to see these posts and, because they do not appear on your news feed, you do not have the power to delete them. This becomes a threat to one's autonomy, for even if they wanted to delete this post, they can not. Furthermore, everyone is entitled to the ability to manage the public presentation of their own self-identity, and in this corrupt personalization, Facebook is giving users new aspects of their identity that may or may not be accurate. <br />
<br />
=== Agency And Accountability ===<br />
Algorithms make "decisions" based on the steps they were designed to follow and the input they received. This can often lead to algorithms as autonomous agents<ref>“Autonomous Agent.” Autonomous Agent - an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/computer-science/autonomous-agent.</ref>, taking decision making responsibilities out of the hands of real people. Useful in terms of efficiency, these autonomous agents are capable of making decisions in a greater frequency than humans. Efficiency is what materializes the baseline for algorithm use in the first place.<br />
<br />
From an ethical standpoint, this type of agency raises many complications, specifically regarding accountability. It is no secret that many aspects of life are run by algorithms. Even events like applying to jobs are often drastically effected by these processes. Information like age, race, status, along with other qualifications, are all fed to algorithms, which then take agency and decide who moves further along in the hiring process and who is left behind. Disregarding inherent biases in this specific scenario, this process still serves to reduce the input of real humans and decrease the number of decisions that they have to make, and what is left over is the fact that autonomous agents are making systematic decisions that have extraordinary impact on people's lives. While the results of the previous example may only culminate to the occasional disregard of a qualified applicant or resentful feelings, this same principle can be much more influential.<br />
<br />
====The Trolley Problem in Practice====<br />
Consider autonomous vehicles, or self-driving cars, for instance. These are highly advanced algorithms that are programmed to make split second decisions with the greatest possible accuracy. In the case of the well-known "Trolley Problem"<ref>Roff, Heather M. “The Folly of Trolleys: Ethical Challenges and Autonomous Vehicles.” Brookings, Brookings, 17 Dec. 2018, www.brookings.edu/research/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/.</ref>, these agents are forced to make a decision jeopardizing one party or another. This decision can easily conclude in the injury or even death of individuals, all at the discretion of a mere program. <br />
<br />
The issue of accountability is then raised, in a situation such as this, because in the eyes of the law, society, and ethical observers, there must be someone held responsible. Attempting to prosecute a program would not be feasible in a legal situation, due to not being able to have a physical representation of the program, like you would a person. However, there are those such as Frances Grodzinsky and Kirsten Martin <ref>Martin, Kirsten. “Ethical Implications and Accountability of Algorithms.” SpringerLink, Springer Netherlands, 7 June 2018, link.springer.com/article/10.1007/s10551-018-3921-3.</ref>, who believe that the designers of an artificial agent, should be responsible for the actions of the program. <ref name="Grodzinsky"> "The ethics of designing artificial agents" by Frances S. Grodzinsky et al, Springer, 2008.</ref> Others contend this point by saying that the blame should be attributed to the users or persons directly involved in the situation. <br />
<br />
These complications will continue to arise, especially as algorithms continue to make autonomous decisions at grander scales and rates. Determining responsibility for the decisions these agents make will continue to be a vexing process, and will no doubt shape in some form many of the advanced algorithms that will be developed in the coming years.<br />
<br />
=== Intentions and Consequences ===<br />
The ethical consequences that are common in algorithm implementations can be either deliberate or unintentional. Instances where an algorithm's intent and outcome differs is noted below. <br />
<br />
==== YouTube Radicalization ====<br />
Scholar and technosociologist Zeynep Tufekci has claimed that "[[YouTube]] may be one of the most powerful radicalizing instruments of the 21st century."<ref name="YouTube Radicalization">[https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html] Tufekci, Zeynep. “YouTube, the Great Radicalizer.” The New York Times, The New York Times, 10 Mar. 2018, www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html.</ref> As YouTube algorithms aim maximize the amount of time that viewers spend watching, it inevitably discovered that the best way to do this was to show videos that slowly "up the stakes" of the subject being watched - from jogging to ultramarathons, from vegetarianism to veganism, from Trump speeches to white supremacist rants.<ref name="YouTube Radicalization" /> Thus, while the intention of Youtube is to keep viewers watching (and bring in advertisement money), they have unintentionally created a site that shows viewers more and more extreme content - contributing to radicalization. Such activity circles back to and produce filters and echo chambers.<br />
<br />
==== Facebook Advertising ====<br />
By taking a closer look at Facebook's algorithm that serves up ads to its users, gender and racial bias is obviously prominent. Using demographic and racial background as factors, Facebook's decides which ads are served up to its users. A team from Northeastern tested to see the algorithm bias in action and by running identical ads with slight tweaks to budget, images, and headings, the ads reached vastly different audiences. Results such as minorities receiving a higher percentage of low-cost housing ads, and women receiving more ads for secretary and nursing jobs <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Although the intent of Facebook may be to reach people that these ads are intended for, the companies that are signing up to advertise with Facebook have stated they did not anticipate this type of filtering when paying for their services. Additionally, although Facebook may believe it is win-win for everyone since advertisers will be getting more interactions with certain audiences targeted, and people will be happy to see ads more relatable to them shown, it is incredibly discriminatory to target people based on factors that are uncontrollable. Facebook needs to adjust its targeted advertising practices by removing racial and gender factors in their algorithms in order to prevent perpetuating stereotypes and placing people in certain boxes by race and gender. This type of algorithm goes against many ethical principles and is important that powerful technology companies are not setting poor examples for others.<br />
<br />
==See also==<br />
{{resource|<br />
*[[Bias in Information]]<br />
*[[Artificial Agents]]<br />
*[[Value Sensitive Design]]<br />
*[[Artificial Intelligence and Technology]]<br />
}}<br />
<br />
== References ==<br />
<references/><br />
[[Category:2019New]]<br />
[[Category:Concepts]]<br />
[[Category:BlueStar2019]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=James_Jones,_Jr.&diff=91437James Jones, Jr.2021-02-11T17:47:26Z<p>WikiSysop: Created page with "alkdjflaksjdlfaslkdfa;ls"</p>
<hr />
<div>alkdjflaksjdlfaslkdfa;ls</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Margaret_Thatcher&diff=91434Margaret Thatcher2021-02-11T17:46:16Z<p>WikiSysop: Created page with "laksjdfl;kasjd;lfkjalskdjf"</p>
<hr />
<div>laksjdfl;kasjd;lfkjalskdjf</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Demonstration&diff=91433Demonstration2021-02-11T17:07:10Z<p>WikiSysop: </p>
<hr />
<div><br />
This is a '''demonstration''' (deh·muhn·strei·shn) of MediaWiki markup code on techniques that SI 410 students can use to format articles more completely. The parts of this article include information on renaming a page by "moving" it to a new page; uploading files that can be used to illustrate articles; linking internally and externally; creating footnotes for references; and assigning categories to individual articles. Each of these techniques is highly valuable and adds significant content to the MediaWiki site.<br />
<br />
== Magic Words == <br />
<br />
Some tags serve special purposes in a MediaWiki. Magic words are strings of text that MediaWiki associates with a return value or function, such as time, site details, or page names. For example, this tag is a "magic word" that, when embedded in an article returns the total number of pages at the wiki site. To date, the total number of pages is {{NUMBEROFPAGES}}.<br />
<br />
This tag returns the total number of edits made at the MediaWiki site. To date, the total number of edits is {{NUMBEROFEDITS}}. This tag can be modified to show how many edits have been made on a particular page. <br />
<br />
To find information about how to use magic words, click [http://www.mediawiki.org/wiki/Help:Magic_words here].<br />
<br />
== Uploading Images ==<br />
<br />
All images embedded in MediaWiki articles must be uploaded. Then the specific name of the uploaded file, along with particular formatting instructions and an appropriate caption, are placed in the text of an article where you wish the image to appear. Recall that captions in MediaWiki should add information, context, and overall value to the article.<br />
<br />
[[File:Greenwich Mean Shadow2.jpg|200px|thumb|right|The instructor feigning anonymity at Greenwich Mean]]<br />
<br />
Here are the instructions for uploading a file: <br />
<br />
# Prepare the file for upload. Make sure the file is exactly as you want it.<br />
# In the sidebar, under “toolbox”, click “Upload file.”<br />
# Click “Browse” next to the “Source filename:” to locate the file on your computer (the name of the “browse” button depends on your web browser).<br />
# Change the “Destination filename:” to something descriptive, if necessary.<br />
# Fill in the “Summary,” if necessary.<br />
# Click the “Upload file” button.<br />
<br />
Here is help on [http://www.mediawiki.org/wiki/Help:Images uploading images].<br />
<br />
<br />
== References ==<br />
<br />
Creating references is an extremely important part of writing a MediaWiki article. The reference system will not function in an article unless a special tag is added near the end of the article, after the point at which the last reference will appear in the text. <br />
<br />
This is the special tag: <pre> <references/></pre><br />
<br />
What follows is a paragraph of text copied from Luciano Floridi's website, which is used to illustrate how to place a footnote in the text. You can view the actual markup in "edit" mode. <br />
<br />
For excellent help on footnote formatting, see the following help page: [http://meta.wikimedia.org/wiki/Help:Footnotes Footnote Help].<br />
<br />
''Luciano Floridi was born in Rome in 1964. He was educated at Rome University La Sapienza, where he graduated in philosophy (laurea) in 1988, first class with distinction. He obtained his MPhil in 1989 and PhD degree in 1990, both from the University of Warwick. Floridi’s research concerns primarily the Philosophy of Information and Information Ethics. Other research interests include Epistemology, Philosophy of Logic, Philosophy of Technology, and the History and Philosophy of Scepticism.'' <ref>Floridi, L. & Turilli, M., The Ethics of Information Transparency, Ethics and Information Technology, 2009, 11.2, 105-112.</ref> ''Since 2008, he is Professor of Philosophy at the University of Hertfordshire – where he holds the Research Chair in Philosophy of Information and the UNESCO Chair of Information and Computer Ethics – and Fellow of St Cross College, University of Oxford, where he is the founder and director of the IEG, Oxford University Information Ethics research Group.''<ref>Insert an appropriate reference here!</ref><br />
<br />
== Linking Techniques ==<br />
<br />
Another important technique is adding links to articles that direct the reader either to another page on the MediaWiki site or to an external website.<br />
<br />
This link takes the reader to the SI 410 MediaWiki page on [[Information Ethics]].<br />
<br />
This link takes the reader to the [[wikipedia:Baseball|Wikipedia article on baseball]]. This easy link (check out the edit page) is possible because MediaWiki and Wikipedia share the same underlying technology platform. <br />
<br />
[[File:Beat_Dead_Horse.jpg|200px|thumb|right|A caption here seems redundant.]]<br />
<br />
Just to beat a dead horse, here is a link to the Wikipedia article on [[Wikipedia:Floridi|Luciano Floridi]]. <br />
<br />
This is a link to [http://www.philosophyofinformation.net/Introduction.html Luciano Floridi’s website].<br />
<br />
It is possible to insert a URL inline without any text associated with it, but the style is messy. It is also possible to insert a URL in such a way that it is numbered like a footnote. But this approach does not add much value to the article. The best way to create a link is to add text to the link and place the link in the proper place of the article.<br />
<br />
== Categories ==<br />
<br />
Given how many students and aliases are logged to the site and how many articles are created in one semester, long lists are not helpful. A better approach is to assign one or more categories to a page, allowing the system to generate an index page that can then be placed anywhere. <br />
<br />
A category is created by creating a page in the Category: namespace. A category page can be created the same way as other wiki pages (see [http://www.mediawiki.org/wiki/Help:Starting_a_new_page Help:Starting a new page]); just add "Category:" before the page title.<br />
<br />
To avoid extra work, try searching within the wiki before creating a new category. The list of all categories can be found in "Special pages" in the "toolbox" box of the sidebar.<br />
<br />
Unlike other wiki pages, it is not possible to rename (move) a category. It is necessary to create a new category and change the Category tag on every page. The new category will not have the older category's page history, which is undesirable if there are many revisions.<br />
<br />
In the SI MediaWiki site, the three major topic categories are concept, person, and object. Students are welcome to experiment with categories, to create new category schemes and set up category pages. It might also be possible to assign categories to students and aliases.<br />
<br />
Warning! There is no way to globally change a set of categories to a new name by moving them, such as you do with pages. Once you start assigning categories, if you change your mind about the name of the category or its spelling, you have to go to every page with the category name and modify it. The list of established categories is available through the special pages.<br />
<br />
== Templates ==<br />
Templates are standard wiki pages whose content is designed to be embedded inside other pages. Templates follow a convention that the name is prefixed with "Template:", assigning it to that namespace; besides this, you can create them like any other wiki page.<br />
<br />
For the SI MediaWiki site, we can use <nowiki>{{citation needed}} and {{Who}}</nowiki> templates to question uncited claims made in an article.<br />
{{quotation|'''Example:''' 65% of people believe in ghosts.<sup>[citation needed]</sup> }}<br />
<br />
See the [http://www.mediawiki.org/wiki/Help:Templates MediaWiki Help:Templates] page for additional information.<br />
<br />
== References ==<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Demonstration&diff=91432Demonstration2021-02-11T17:06:32Z<p>WikiSysop: </p>
<hr />
<div><br />
This page is a '''demonstration''' (deh·muhn·strei·shn) of MediaWiki markup code on techniques that SI 410 students can use to format articles more completely. The parts of this article include information on renaming a page by "moving" it to a new page; uploading files that can be used to illustrate articles; linking internally and externally; creating footnotes for references; and assigning categories to individual articles. Each of these techniques is highly valuable and adds significant content to the MediaWiki site.<br />
<br />
== Magic Words == <br />
<br />
Some tags serve special purposes in a MediaWiki. Magic words are strings of text that MediaWiki associates with a return value or function, such as time, site details, or page names. For example, this tag is a "magic word" that, when embedded in an article returns the total number of pages at the wiki site. To date, the total number of pages is {{NUMBEROFPAGES}}.<br />
<br />
This tag returns the total number of edits made at the MediaWiki site. To date, the total number of edits is {{NUMBEROFEDITS}}. This tag can be modified to show how many edits have been made on a particular page. <br />
<br />
To find information about how to use magic words, click [http://www.mediawiki.org/wiki/Help:Magic_words here].<br />
<br />
== Uploading Images ==<br />
<br />
All images embedded in MediaWiki articles must be uploaded. Then the specific name of the uploaded file, along with particular formatting instructions and an appropriate caption, are placed in the text of an article where you wish the image to appear. Recall that captions in MediaWiki should add information, context, and overall value to the article.<br />
<br />
[[File:Greenwich Mean Shadow2.jpg|200px|thumb|right|The instructor feigning anonymity at Greenwich Mean]]<br />
<br />
Here are the instructions for uploading a file: <br />
<br />
# Prepare the file for upload. Make sure the file is exactly as you want it.<br />
# In the sidebar, under “toolbox”, click “Upload file.”<br />
# Click “Browse” next to the “Source filename:” to locate the file on your computer (the name of the “browse” button depends on your web browser).<br />
# Change the “Destination filename:” to something descriptive, if necessary.<br />
# Fill in the “Summary,” if necessary.<br />
# Click the “Upload file” button.<br />
<br />
Here is help on [http://www.mediawiki.org/wiki/Help:Images uploading images].<br />
<br />
<br />
== References ==<br />
<br />
Creating references is an extremely important part of writing a MediaWiki article. The reference system will not function in an article unless a special tag is added near the end of the article, after the point at which the last reference will appear in the text. <br />
<br />
This is the special tag: <pre> <references/></pre><br />
<br />
What follows is a paragraph of text copied from Luciano Floridi's website, which is used to illustrate how to place a footnote in the text. You can view the actual markup in "edit" mode. <br />
<br />
For excellent help on footnote formatting, see the following help page: [http://meta.wikimedia.org/wiki/Help:Footnotes Footnote Help].<br />
<br />
''Luciano Floridi was born in Rome in 1964. He was educated at Rome University La Sapienza, where he graduated in philosophy (laurea) in 1988, first class with distinction. He obtained his MPhil in 1989 and PhD degree in 1990, both from the University of Warwick. Floridi’s research concerns primarily the Philosophy of Information and Information Ethics. Other research interests include Epistemology, Philosophy of Logic, Philosophy of Technology, and the History and Philosophy of Scepticism.'' <ref>Floridi, L. & Turilli, M., The Ethics of Information Transparency, Ethics and Information Technology, 2009, 11.2, 105-112.</ref> ''Since 2008, he is Professor of Philosophy at the University of Hertfordshire – where he holds the Research Chair in Philosophy of Information and the UNESCO Chair of Information and Computer Ethics – and Fellow of St Cross College, University of Oxford, where he is the founder and director of the IEG, Oxford University Information Ethics research Group.''<ref>Insert an appropriate reference here!</ref><br />
<br />
== Linking Techniques ==<br />
<br />
Another important technique is adding links to articles that direct the reader either to another page on the MediaWiki site or to an external website.<br />
<br />
This link takes the reader to the SI 410 MediaWiki page on [[Information Ethics]].<br />
<br />
This link takes the reader to the [[wikipedia:Baseball|Wikipedia article on baseball]]. This easy link (check out the edit page) is possible because MediaWiki and Wikipedia share the same underlying technology platform. <br />
<br />
[[File:Beat_Dead_Horse.jpg|200px|thumb|right|A caption here seems redundant.]]<br />
<br />
Just to beat a dead horse, here is a link to the Wikipedia article on [[Wikipedia:Floridi|Luciano Floridi]]. <br />
<br />
This is a link to [http://www.philosophyofinformation.net/Introduction.html Luciano Floridi’s website].<br />
<br />
It is possible to insert a URL inline without any text associated with it, but the style is messy. It is also possible to insert a URL in such a way that it is numbered like a footnote. But this approach does not add much value to the article. The best way to create a link is to add text to the link and place the link in the proper place of the article.<br />
<br />
== Categories ==<br />
<br />
Given how many students and aliases are logged to the site and how many articles are created in one semester, long lists are not helpful. A better approach is to assign one or more categories to a page, allowing the system to generate an index page that can then be placed anywhere. <br />
<br />
A category is created by creating a page in the Category: namespace. A category page can be created the same way as other wiki pages (see [http://www.mediawiki.org/wiki/Help:Starting_a_new_page Help:Starting a new page]); just add "Category:" before the page title.<br />
<br />
To avoid extra work, try searching within the wiki before creating a new category. The list of all categories can be found in "Special pages" in the "toolbox" box of the sidebar.<br />
<br />
Unlike other wiki pages, it is not possible to rename (move) a category. It is necessary to create a new category and change the Category tag on every page. The new category will not have the older category's page history, which is undesirable if there are many revisions.<br />
<br />
In the SI MediaWiki site, the three major topic categories are concept, person, and object. Students are welcome to experiment with categories, to create new category schemes and set up category pages. It might also be possible to assign categories to students and aliases.<br />
<br />
Warning! There is no way to globally change a set of categories to a new name by moving them, such as you do with pages. Once you start assigning categories, if you change your mind about the name of the category or its spelling, you have to go to every page with the category name and modify it. The list of established categories is available through the special pages.<br />
<br />
== Templates ==<br />
Templates are standard wiki pages whose content is designed to be embedded inside other pages. Templates follow a convention that the name is prefixed with "Template:", assigning it to that namespace; besides this, you can create them like any other wiki page.<br />
<br />
For the SI MediaWiki site, we can use <nowiki>{{citation needed}} and {{Who}}</nowiki> templates to question uncited claims made in an article.<br />
{{quotation|'''Example:''' 65% of people believe in ghosts.<sup>[citation needed]</sup> }}<br />
<br />
See the [http://www.mediawiki.org/wiki/Help:Templates MediaWiki Help:Templates] page for additional information.<br />
<br />
== References ==<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Demonstration&diff=91431Demonstration2021-02-11T17:04:45Z<p>WikiSysop: </p>
<hr />
<div><br />
This page is a demonstration of MediaWiki markup code on techniques that SI 410 students can use to format articles more completely. The parts of this article include information on renaming a page by "moving" it to a new page; uploading files that can be used to illustrate articles; linking internally and externally; creating footnotes for references; and assigning categories to individual articles. Each of these techniques is highly valuable and adds significant content to the MediaWiki site.<br />
<br />
== Magic Words == <br />
<br />
Some tags serve special purposes in a MediaWiki. Magic words are strings of text that MediaWiki associates with a return value or function, such as time, site details, or page names. For example, this tag is a "magic word" that, when embedded in an article returns the total number of pages at the wiki site. To date, the total number of pages is {{NUMBEROFPAGES}}.<br />
<br />
This tag returns the total number of edits made at the MediaWiki site. To date, the total number of edits is {{NUMBEROFEDITS}}. This tag can be modified to show how many edits have been made on a particular page. <br />
<br />
To find information about how to use magic words, click [http://www.mediawiki.org/wiki/Help:Magic_words here].<br />
<br />
== Uploading Images ==<br />
<br />
All images embedded in MediaWiki articles must be uploaded. Then the specific name of the uploaded file, along with particular formatting instructions and an appropriate caption, are placed in the text of an article where you wish the image to appear. Recall that captions in MediaWiki should add information, context, and overall value to the article.<br />
<br />
[[File:Greenwich Mean Shadow2.jpg|200px|thumb|right|The instructor feigning anonymity at Greenwich Mean]]<br />
<br />
Here are the instructions for uploading a file: <br />
<br />
# Prepare the file for upload. Make sure the file is exactly as you want it.<br />
# In the sidebar, under “toolbox”, click “Upload file.”<br />
# Click “Browse” next to the “Source filename:” to locate the file on your computer (the name of the “browse” button depends on your web browser).<br />
# Change the “Destination filename:” to something descriptive, if necessary.<br />
# Fill in the “Summary,” if necessary.<br />
# Click the “Upload file” button.<br />
<br />
Here is help on [http://www.mediawiki.org/wiki/Help:Images uploading images].<br />
<br />
<br />
== References ==<br />
<br />
Creating references is an extremely important part of writing a MediaWiki article. The reference system will not function in an article unless a special tag is added near the end of the article, after the point at which the last reference will appear in the text. <br />
<br />
This is the special tag: <pre> <references/></pre><br />
<br />
What follows is a paragraph of text copied from Luciano Floridi's website, which is used to illustrate how to place a footnote in the text. You can view the actual markup in "edit" mode. <br />
<br />
For excellent help on footnote formatting, see the following help page: [http://meta.wikimedia.org/wiki/Help:Footnotes Footnote Help].<br />
<br />
''Luciano Floridi was born in Rome in 1964. He was educated at Rome University La Sapienza, where he graduated in philosophy (laurea) in 1988, first class with distinction. He obtained his MPhil in 1989 and PhD degree in 1990, both from the University of Warwick. Floridi’s research concerns primarily the Philosophy of Information and Information Ethics. Other research interests include Epistemology, Philosophy of Logic, Philosophy of Technology, and the History and Philosophy of Scepticism.'' <ref>Floridi, L. & Turilli, M., The Ethics of Information Transparency, Ethics and Information Technology, 2009, 11.2, 105-112.</ref> ''Since 2008, he is Professor of Philosophy at the University of Hertfordshire – where he holds the Research Chair in Philosophy of Information and the UNESCO Chair of Information and Computer Ethics – and Fellow of St Cross College, University of Oxford, where he is the founder and director of the IEG, Oxford University Information Ethics research Group.''<ref>Insert an appropriate reference here!</ref><br />
<br />
== Linking Techniques ==<br />
<br />
Another important technique is adding links to articles that direct the reader either to another page on the MediaWiki site or to an external website.<br />
<br />
This link takes the reader to the SI 410 MediaWiki page on [[Information Ethics]].<br />
<br />
This link takes the reader to the [[wikipedia:Baseball|Wikipedia article on baseball]]. This easy link (check out the edit page) is possible because MediaWiki and Wikipedia share the same underlying technology platform. <br />
<br />
[[File:Beat_Dead_Horse.jpg|200px|thumb|right|A caption here seems redundant.]]<br />
<br />
Just to beat a dead horse, here is a link to the Wikipedia article on [[Wikipedia:Floridi|Luciano Floridi]]. <br />
<br />
This is a link to [http://www.philosophyofinformation.net/Introduction.html Luciano Floridi’s website].<br />
<br />
It is possible to insert a URL inline without any text associated with it, but the style is messy. It is also possible to insert a URL in such a way that it is numbered like a footnote. But this approach does not add much value to the article. The best way to create a link is to add text to the link and place the link in the proper place of the article.<br />
<br />
== Categories ==<br />
<br />
Given how many students and aliases are logged to the site and how many articles are created in one semester, long lists are not helpful. A better approach is to assign one or more categories to a page, allowing the system to generate an index page that can then be placed anywhere. <br />
<br />
A category is created by creating a page in the Category: namespace. A category page can be created the same way as other wiki pages (see [http://www.mediawiki.org/wiki/Help:Starting_a_new_page Help:Starting a new page]); just add "Category:" before the page title.<br />
<br />
To avoid extra work, try searching within the wiki before creating a new category. The list of all categories can be found in "Special pages" in the "toolbox" box of the sidebar.<br />
<br />
Unlike other wiki pages, it is not possible to rename (move) a category. It is necessary to create a new category and change the Category tag on every page. The new category will not have the older category's page history, which is undesirable if there are many revisions.<br />
<br />
In the SI MediaWiki site, the three major topic categories are concept, person, and object. Students are welcome to experiment with categories, to create new category schemes and set up category pages. It might also be possible to assign categories to students and aliases.<br />
<br />
Warning! There is no way to globally change a set of categories to a new name by moving them, such as you do with pages. Once you start assigning categories, if you change your mind about the name of the category or its spelling, you have to go to every page with the category name and modify it. The list of established categories is available through the special pages.<br />
<br />
== Templates ==<br />
Templates are standard wiki pages whose content is designed to be embedded inside other pages. Templates follow a convention that the name is prefixed with "Template:", assigning it to that namespace; besides this, you can create them like any other wiki page.<br />
<br />
For the SI MediaWiki site, we can use <nowiki>{{citation needed}} and {{Who}}</nowiki> templates to question uncited claims made in an article.<br />
{{quotation|'''Example:''' 65% of people believe in ghosts.<sup>[citation needed]</sup> }}<br />
<br />
See the [http://www.mediawiki.org/wiki/Help:Templates MediaWiki Help:Templates] page for additional information.<br />
<br />
== References ==<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Demonstration&diff=91430Demonstration2021-02-11T17:04:01Z<p>WikiSysop: </p>
<hr />
<div><br />
This page conveys information and MediaWiki markup code on techniques that SI 410 students can use to format articles more completely. The parts of this article include information on renaming a page by "moving" it to a new page; uploading files that can be used to illustrate articles; linking internally and externally; creating footnotes for references; and assigning categories to individual articles. Each of these techniques is highly valuable and adds significant content to the MediaWiki site.<br />
<br />
== Magic Words == <br />
<br />
Some tags serve special purposes in a MediaWiki. Magic words are strings of text that MediaWiki associates with a return value or function, such as time, site details, or page names. For example, this tag is a "magic word" that, when embedded in an article returns the total number of pages at the wiki site. To date, the total number of pages is {{NUMBEROFPAGES}}.<br />
<br />
This tag returns the total number of edits made at the MediaWiki site. To date, the total number of edits is {{NUMBEROFEDITS}}. This tag can be modified to show how many edits have been made on a particular page. <br />
<br />
To find information about how to use magic words, click [http://www.mediawiki.org/wiki/Help:Magic_words here].<br />
<br />
== Uploading Images ==<br />
<br />
All images embedded in MediaWiki articles must be uploaded. Then the specific name of the uploaded file, along with particular formatting instructions and an appropriate caption, are placed in the text of an article where you wish the image to appear. Recall that captions in MediaWiki should add information, context, and overall value to the article.<br />
<br />
[[File:Greenwich Mean Shadow2.jpg|200px|thumb|right|The instructor feigning anonymity at Greenwich Mean]]<br />
<br />
Here are the instructions for uploading a file: <br />
<br />
# Prepare the file for upload. Make sure the file is exactly as you want it.<br />
# In the sidebar, under “toolbox”, click “Upload file.”<br />
# Click “Browse” next to the “Source filename:” to locate the file on your computer (the name of the “browse” button depends on your web browser).<br />
# Change the “Destination filename:” to something descriptive, if necessary.<br />
# Fill in the “Summary,” if necessary.<br />
# Click the “Upload file” button.<br />
<br />
Here is help on [http://www.mediawiki.org/wiki/Help:Images uploading images].<br />
<br />
<br />
== References ==<br />
<br />
Creating references is an extremely important part of writing a MediaWiki article. The reference system will not function in an article unless a special tag is added near the end of the article, after the point at which the last reference will appear in the text. <br />
<br />
This is the special tag: <pre> <references/></pre><br />
<br />
What follows is a paragraph of text copied from Luciano Floridi's website, which is used to illustrate how to place a footnote in the text. You can view the actual markup in "edit" mode. <br />
<br />
For excellent help on footnote formatting, see the following help page: [http://meta.wikimedia.org/wiki/Help:Footnotes Footnote Help].<br />
<br />
''Luciano Floridi was born in Rome in 1964. He was educated at Rome University La Sapienza, where he graduated in philosophy (laurea) in 1988, first class with distinction. He obtained his MPhil in 1989 and PhD degree in 1990, both from the University of Warwick. Floridi’s research concerns primarily the Philosophy of Information and Information Ethics. Other research interests include Epistemology, Philosophy of Logic, Philosophy of Technology, and the History and Philosophy of Scepticism.'' <ref>Floridi, L. & Turilli, M., The Ethics of Information Transparency, Ethics and Information Technology, 2009, 11.2, 105-112.</ref> ''Since 2008, he is Professor of Philosophy at the University of Hertfordshire – where he holds the Research Chair in Philosophy of Information and the UNESCO Chair of Information and Computer Ethics – and Fellow of St Cross College, University of Oxford, where he is the founder and director of the IEG, Oxford University Information Ethics research Group.''<ref>Insert an appropriate reference here!</ref><br />
<br />
== Linking Techniques ==<br />
<br />
Another important technique is adding links to articles that direct the reader either to another page on the MediaWiki site or to an external website.<br />
<br />
This link takes the reader to the SI 410 MediaWiki page on [[Information Ethics]].<br />
<br />
This link takes the reader to the [[wikipedia:Baseball|Wikipedia article on baseball]]. This easy link (check out the edit page) is possible because MediaWiki and Wikipedia share the same underlying technology platform. <br />
<br />
[[File:Beat_Dead_Horse.jpg|200px|thumb|right|A caption here seems redundant.]]<br />
<br />
Just to beat a dead horse, here is a link to the Wikipedia article on [[Wikipedia:Floridi|Luciano Floridi]]. <br />
<br />
This is a link to [http://www.philosophyofinformation.net/Introduction.html Luciano Floridi’s website].<br />
<br />
It is possible to insert a URL inline without any text associated with it, but the style is messy. It is also possible to insert a URL in such a way that it is numbered like a footnote. But this approach does not add much value to the article. The best way to create a link is to add text to the link and place the link in the proper place of the article.<br />
<br />
== Categories ==<br />
<br />
Given how many students and aliases are logged to the site and how many articles are created in one semester, long lists are not helpful. A better approach is to assign one or more categories to a page, allowing the system to generate an index page that can then be placed anywhere. <br />
<br />
A category is created by creating a page in the Category: namespace. A category page can be created the same way as other wiki pages (see [http://www.mediawiki.org/wiki/Help:Starting_a_new_page Help:Starting a new page]); just add "Category:" before the page title.<br />
<br />
To avoid extra work, try searching within the wiki before creating a new category. The list of all categories can be found in "Special pages" in the "toolbox" box of the sidebar.<br />
<br />
Unlike other wiki pages, it is not possible to rename (move) a category. It is necessary to create a new category and change the Category tag on every page. The new category will not have the older category's page history, which is undesirable if there are many revisions.<br />
<br />
In the SI MediaWiki site, the three major topic categories are concept, person, and object. Students are welcome to experiment with categories, to create new category schemes and set up category pages. It might also be possible to assign categories to students and aliases.<br />
<br />
Warning! There is no way to globally change a set of categories to a new name by moving them, such as you do with pages. Once you start assigning categories, if you change your mind about the name of the category or its spelling, you have to go to every page with the category name and modify it. The list of established categories is available through the special pages.<br />
<br />
== Templates ==<br />
Templates are standard wiki pages whose content is designed to be embedded inside other pages. Templates follow a convention that the name is prefixed with "Template:", assigning it to that namespace; besides this, you can create them like any other wiki page.<br />
<br />
For the SI MediaWiki site, we can use <nowiki>{{citation needed}} and {{Who}}</nowiki> templates to question uncited claims made in an article.<br />
{{quotation|'''Example:''' 65% of people believe in ghosts.<sup>[citation needed]</sup> }}<br />
<br />
See the [http://www.mediawiki.org/wiki/Help:Templates MediaWiki Help:Templates] page for additional information.<br />
<br />
== References ==<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Algorithms&diff=91429Algorithms2021-02-11T17:01:30Z<p>WikiSysop: Removed protection from "Algorithms"</p>
<hr />
<div>[[File:Algorithm.png|200px|thumb|right]]<br />
{{Nav-Bar|Topics##}}<br><br />
An '''Algorithm''' is defined as a set of precise steps and distinct states used to express the detailed structure of a program or the order of events that occurred in a system <ref>Cormen, Thomas H. et al. (2009). ''Introduction to Algorithms;'. MIT Press.</ref>.Algorithms are involved in many aspects of daily life and in complex computer science concepts. They often use repetition of operations to allow people and machines to execute tasks more efficiently by executing tasks faster and using fewer resources such as memory. On a basic level, an algorithm is a system working through different iterations of a process<ref>Lim, Brian (December 7, 2016). [e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/ "A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life)"] ''e27''.</ref>. They can help turn systematic and tedious tasks into fast, automated processes. Large companies particularly value robust algorithms because their infrastructure depends on efficiency to remain profitable on a massive scale<ref>Rastogi, Rajeev, and Kyuseok Shim (1999). "Scalable algorithms for mining large databases." ''Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining''. ACM.</ref>.<br />
<br />
Processes like cooking a meal or reading a manual to assemble a new piece of furniture are examples of algorithms in everyday life<ref>T.C. (August 29, 2017). [https://www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms "What Are Algorithms?"] ''The Economist''. Retrieved April 28, 2019.</ref>. Algorithms are grounded in logic. The increase in their logical complexity via advancements in technology and human effort have provided the foundations for technological concepts such as artificial intelligence and machine learning<ref>McClelland, Calum. (December 4, 2017). [https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991 "The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning"]. ''Medium''. Retrieved April 28, 2019.</ref>. The influence of algorithms is pervasive and in computer science so it leads to an increase in ethical concerns in the areas of bias, privacy, and accountability.<br />
<br />
<br />
== History ==<br />
The earliest known algorithms stem back to 1700 BC when the Egyptians created them for quick multiplication and division<ref>Stepanov, A. and Rose, D. (2014).<br />
''From Mathematics to Generic Programming: The First Algorithm''. [http://www.informit.com/articles/article.aspx?p=2264460 "Chapter 2"]. Retrieved April 28, 2019.</ref>. Since then, ancient Babylonians in 300 BC created algorithms to track farming and livestock using square roots. Following this, steady advancement gave birth to fundamental mathematical algorithm families like algebra, shaping the field of mathematics with its all-purpose formulas. The man often accredited as “The Father of Algebra,” Muhammad ibn Mūsa al-Khwarizmī, was also the one who gave English the word “algorithm” around 850 AD, as he wrote a book ''Al-Khwarizmi on the Hindu Art of Reckoning'', which in Latin translates to ''Algoritmi de Numero Indorum''. The English word [https://www.digit.in/features/science-and-technology/the-origin-of-algorithms-30045.html "algorithm"] was adopted from this title.<br />
<br />
A myriad of fundamental algorithms have been developed throughout history, ranging from pure mathematics to important computer science stalwarts, extending from ancient times up through the modern day. The computer revolution led to algorithms that can filter and personalize results based on the user.<br />
<br />
The [https://en.wikipedia.org/wiki/Timeline_of_algorithms Algorithm Timeline] outlines the advancements in the development of the algorithm as well as a number of the well-known algorithms developed from the Medieval Period until modern day.<br />
<br />
=== Computation ===<br />
Another cornerstone for algorithms comes from [https://en.wikipedia.org/wiki/Alan_Turing Alan Turing] and his contributions to cognitive and [https://en.wikipedia.org/wiki/Computer_science computer science]. Turing conceptualized the concept of cognition and designed ways to emulate human cognition with machines. This process turned the human thought process into mathematical algorithms and it led to the development of Turing Machines. It capitalized on these theoretical algorithms to perform unique functions and the development of computers. As their name suggests, computers utilized specific rules or algorithms to compute and it is these machines (or sometimes people)<ref>[crgis.ndc.nasa.gov/historic/Human_Computers "Human Computers"]. ''NASA Cultural Resources''. Retrieved April 28, 2019.</ref> that most often relate to the concept of algorithms that is used today. With the advent of mechanical computers, the computer science field paved the way for algorithms to run the world as they do now by calculating and controlling an immense quantity of facets of daily life. To this day, Turing machines are a main area of study in the theory of computation.<br />
<br />
=== Advancements In Algorithms ===<br />
In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity. Advanced algorithms, such as artificial intelligence is defined as utilizing machine learning capabilities.<ref>Anyoha, Rockwell (August 28, 2017). [http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/ "The History of Artificial Intelligence"]. ''Science in the News''. Harvard. Retrieved April 28, 2019.</ref> This level of algorithmic improvement provided the foundation for more technological advancement.<br />
[[File:machineLearning.png|500px]]<br />
<br />
The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.<br />
<br />
== Classifications ==<br />
There are many different classifications of algorithms, some are more well-suited for particular families of computational problems than others. In many cases, the algorithm one chooses to make for a given problem will have tradeoffs dealing with time complexity and memory usage.<br />
<br />
==== Recursive Algorithms ====<br />
<br />
A [https://en.wikipedia.org/wiki/Recursion_(computer_science) recursive algorithm] is an algorithm that calls itself with decreasing values in order to reach a pre-defined base case solution. The base case solution determines the values that are sent back up the recursive stack to determine the final outcome of the algorithm. It follows the principle of solving subproblems to solve the larger problem since once the base case solution is reached, the algorithm works upwards to fit the solution into the larger subproblem. The base case must be present, otherwise the recursive function will never stop calling itself, creating an infinite loop. Since recursion involves numerous function calls, it is one of the main sources of stack overflow. With recursive calls, programs have to save more stacks despite a lack of available space. Further, some recursive functions require additional computations even after the recursive call, adding to the consumption speed and memory. 'Tail Recursive' functions are an efficient solution to this, wherein recursive calls happen at the very end of the function, allowing only one stack to be saved throughout the function calls.<br />
<br />
Due to the recurring memory stack frames that are created with each call, recursive algorithms generally require more memory and computation power. However, they are still viewed as simplistic and succinct ways to write elaborate algorithms. <br />
<br />
==== Serial, Parallel or Distributed ====<br />
A [https://en.wikipedia.org/wiki/Sequential_algorithm serial algorithm] is an algorithm in which calculation is done in sequential manners on one core, it follows a defined order in solving a problem. Parallel algorithms utilizes the fact that modern computers have more than one cores, so that computations that are not interdependent could be calculated on separate cores. This is referred to as multi-threaded algorithm where each thread is a series of executing commands and they are inter-weaved to ensure correct output without deadlock. A deadlock occurs when there are interdependence between more than one thread so that none of the threads can continue until one of the other threads continues. Parallel algorithm is important that it allows more than one program to run at the same time by leveraging a computer's available resources otherwise would not have been possible with serial algorithm. Finally, distributed algorithm is similar to parallel algorithm in that it allows multiple programs to run at once with the exception that instead of leveraging multiple cores in a single computer it leverages multiple computers that communicates through a computer network. Similar to how parallel algorithm builds on serial algorithm with the added complexity of synchronizing threads to prevent deadlock and ensure correct outputs, distributed algorithm builds on parallel algorithm with the added complexity of managing communication latency and defining order since it is impossible to synchronize every computer's clock in a distributed system without significant compromises. A distributed algorithm provides extra reliability in that data are stored in more than one location so that one failure would not result in loss of data and by doing computation in multiple computer it can potentially have even faster computational speed.<br />
<br />
==== Deterministic vs Non-Deterministic ====<br />
Deterministic algorithms are those that solve problems with exact precision and ordering so a given input would produce the same output every time. Non-deterministic algorithms either have data races or utilize certain randomization, so the same input could have a myriad of outputs. Non-deterministic algorithms can be represented by flowcharts, programming languages, and machine language programs. However, they vary in that they are capable of using a multiple-valued function whose values are the positive integers less than or equal to itself. In addition, all points of termination are labeled as successes or failures. The terminology "non-deterministic" does not imply randomness, of rather a kind of free will<ref>"<br />
Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref><br />
<br />
==== Exact vs Approximation ====<br />
Some algorithms are implemented to solve for the exact solutions of a problem, whereas some problems are implemented for an approximation or heuristic. Approximation is important in which heuristics could provide an answer that is good enough such that the excessive computational time necessary to find the actual solution is not warranted since one would get minimal gains while expanding a great deal of resources. An example where an approximation is warranted is the traveling salesman's algorithm in which has computational complexity of O(n!), so an heuristic is necessary since some high values of n are not even possible to calculate.<br />
<br />
==== Brute Force or Exhaustive Search ====<br />
A brute force algorithm is the most "naive" approach one can take in attempting to solve a particular problem. A solution is reached by searching through every single possible outcome before arriving at an answer. In terms of complexity or Big-O notation, brute force algorithms typically represent the highest order complexity compared to other potential solutions for a given problem. While brute force algorithms may not be considered the most efficient option for solving computational problems, they do offer reliability as well as a guarantee that a solution to a given problem will eventually be found.<br />
<br />
An example of a brute force algorithm would be trying all combinations of a 4-digit passcode, in order to crack into a target's smartphone.<br />
<br />
==== Divide and Conquer ====<br />
A divide and conquer algorithm divides a problem into smaller sub-problems then conquer each smaller problems before merging them together to solve the original problem. In terms of efficiency and the Big-O notation, the Divide and Conquer fares better than Brute Force but is still relatively inefficient compared to other more complex algorithms. An example of divide and conquer is merge sort wherein a list is split into smaller sorted lists and then merged together to sort the original list.<br />
<br />
Examples of Divide and Conquer algorithms would be the sorting algorithm [https://en.wikipedia.org/wiki/Merge_sort Merge Sort], and the searching algorithm [https://en.wikipedia.org/wiki/Binary_search_algorithm Binary Search]<ref>[https://www.geeksforgeeks.org/divide-and-conquer-algorithm-introduction "Divide and Conquer Algorithms"]. ''Geeks for Geeks''. Retrieved April 28, 2019.</ref>.<br />
<br />
====Dynamic Programming====<br />
[https://.wikipedia.org/wiki/Dynamic_programming Dynamic programming] takes advantage of overlapping subproblems to more efficiently solve a larger computational problem. The algorithm first solves less complex subproblems and stores their solutions in memory. Then more complex problems will find these solutions using some method of lookup to find the solution and implement it in the more complex problem to find its own solution. The method of lookup enables solutions to be computed once and used multiple times. This method reduces the time complexity from exponential to polynomial.<br />
<br />
An example of a common problem that can be solved by Dynamic Programming is the [https://en.wikipedia.org/wiki/Knapsack_problem#Dynamic_programming_in-advance_algorithm 0-1 Knapsack Problem].<br />
<br />
==== Backtracking ====<br />
A backtracking algorithm is similar to brute force with the exception that as soon as it reaches a node where a solution could never be reached from said node on, it prunes all the subsequent node and backtracks to the closest node that has the possibility to be right. Pruning in this context means neglecting the failed branch as a potential solution branch in all further searches, reducing the scope of the possible solution set and eventually guiding the program to the right outcome.<br />
<br />
An example of some problems that can be solved by algorithms that take advantage of backtracking is solving [[Wikipedia:Sudoku|Sudoko]], or the [[Wikipedia:Eight_queens_puzzle|N-Queens Problem]].<br />
<br />
====Greedy Algorithm====<br />
An intuitive approach to design algorithms that don’t always yield the optimal solution. This approach required a collection of candidates, or options, in which the algorithm selects in order to satisfy a given predicate. Greedy algorithms can either favor the least element in the collection or the greatest in order to satisfy the predicate<ref>[https://brilliant.org/wiki/greedy-algorithm/ "Greedy Algorithms"]. ''Brilliant Math & Science Wiki''. Retrieved April 28, 2019.</ref>.<br />
<br />
An example of a greedy algorithm may take the form of selecting coins to make change in a transaction. The collection includes the official coins of the U.S. currency (25 cents, 10 cents, 5 cents, and 1 cent) and the predicate would be to make change of 11 cents. The greedy algorithm will select the greatest of our collection to approach 11, but not past it. The algorithm will first select a dime, then a penny, then end when 11 cents has been made.<br />
<br />
== Complexity and Big-O Notation ==<br />
[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]<br />
Measuring the efficiency of an algorithm is standardized by checking how well it grows with more inputs. Computer scientists calculate how much computational time increases with an increasing number of inputs. Since this form of measurement merely intends to see how well an algorithm grows, the constants are left out since with high inputs these constants are negligible anyways. Big-O notation specifically describes the worst-case scenario and measures the time or space the algorithm uses<ref name = 'Big-O'>[ Bell, Rob. [https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/ "A Beginner's Guide to Big O Notation"]. Retrieved April 28, 2019.</ref>. Big-O notation can be broken down into order of growth algorithms such as O(1), O(N), and O(log N), with each notation representing different orders of growth. The later, log algorithms -commonly referred to as logarithms, are bit more complex than the rest, log algorithms take a median from a data set and compare it to a target value, the algorithm continues to halve the data as long as the median is higher or lower than the target value<ref name='Big-O'></ref>. An algorithm with a higher Big-O is less efficient at large scales, for example in general a O(N) algorithm will run slower than a O(1) algorithm, and this difference will be more and more apparent, the larger the number of inputs.<br />
<br />
== Artificial Intelligence Algorithms ==<br />
=== Clustering ===<br />
Clustering is a [https://en.wikipedia.org/wiki/Machine_learning Machine Learning] technique in which, data is segregated into groups called clusters through an algorithm, given a set of data points. These clustering algorithms classify the data based on various criteria, but the fundamental premise is that data points with similarities will belong in the same group, that must be dissimilar to other groups. There are numerous clustering algorithms including K-Means clustering, Mean-Shift Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) Clustering, EM (Expectation Maximization) Clustering, Agglomerative Hierarchical Clustering. <ref name="Clustering">Seif, George (February 5, 2018). [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 "The 5 Clustering Algorithms Data Scientists Need To Know"]. Retrieved April 28, 2019.</ref><br />
<br />
==== K-Means Clustering ====<br />
K-means clustering is the most widely known and used algorithm out of all the clustering algorithms. It involves pre-determining a target number - ''k'', which represents the number of centroids needed in the dataset. A centroid refers to the predicted center of the cluster. It then identifies the data points nearest to the center to form each cluster, while keeping ''k'' as small as possible. <ref>Garbade, Michael J. (September 12, 2018). [https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1 "Understanding K-Means Clustering in Machine Learning"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> K-Means clustering is considered to be a fast algorithm, due to the minimal computations it requires. It has a Big-O complexity of O(''n''). <ref name="Clustering"></ref> <br />
<br />
==== Mean-Shift Clustering ====<br />
Mean-Shift Clustering, also known as Mode-Seeking, is an algorithm where datapoints are grouped into clusters, through the process of iteratively shifting all the points towards their mode. The mode of a dataset is defined as the most occurring value in that particular dataset, or in graphical terms, the point where the density of data points is the highest. Therefore, the algorithm moves, or "shifts" each point closer to its closest centroid, the direction of which is determined by the density of the nearby points. Therefore, each iteration of the program moves each point closer to where all the other points are, eventually forming a cluster center. The key difference between Mean-Shift and K-Means clustering is that K-Means requires the number ''k'' to be set beforehand, whereas the Mean-Shift algorithm creates clusters on the go without necessarily determining how many will be formed. <ref>[http://www.chioka.in/meanshift-algorithm-for-the-rest-of-us-python/ "Meanshift Algorithm for the Rest of Us (Python)"], May 14, 2016. Retrieved April 28, 2019.</ref> Usually, the Big-O complexity of such an algorithm is O(''Tn^2''), where ''T'' refers to the number of iterations in the algorithm. <ref>Thirumuruganathan, Saravanan (April 1, 2010). [https://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/ "Introduction To Mean Shift Algorithm"]. Retrieved April 28, 2019.</ref><br />
<br />
==== DBSCAN Clustering ====<br />
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm that groups together nearby datapoints based on a measure of distance, often [https://en.wikipedia.org/wiki/Euclidean_distance Euclidean distance] and a minimal number of points. The algorithm also differentiates outliers if they are in low density areas. The algorithm requires two parameters - ''eps'' and ''minPoints''. The decision on what to set these parameters as depends from dataset to dataset, and requires a fundamental understanding of the context of the dataset being used. The ''eps'' should be picked based on the dataset distance and while generally small ''eps'' values are desirable, if the set value is too small there is a danger that a portion of the data will go unclustered. Conversely, if the value set is too large, too many of the points might get grouped into the same cluster. The minPoints parameter is usually derived from the parameter ''D'', following that minPoints ≥ ''D + 1'' where ''D'' measures the number of dimensions in the data. <ref>Salton do Prado, Kelvin (April 1, 2017). [https://towardsdatascience.com/how-dbscan-works-and-why-should-i-use-it-443b4a191c80 "How DBSCAN works and why should we use it?"]. ''Towards Data Science''. Retrieved April 28, 2019.</ref> The average run-time complexity of a DBSCAN algorithm is O(''n log n'') whereas it's worst-case complexity can be O(''n^2'').<br />
<br />
==== EM Clustering ====<br />
Expectation Maximization or EM Clustering is similar to the K-Means clustering technique. The EM Clustering method takes forward the basic principles of K-Means Clustering in two primary ways:<br />
1) The EM Clustering method aims to calculate what datapoints belong in what cluster through one or more probability distributions, instead of trying to simply calculate and maximize the difference in mean points.<br />
2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.<br />
<br />
Essentially, the EM clustering method approximates the distribution of each point based on different probability distributions and at the end of it, each observation has a certain level of probability of belonging to a particular cluster. Ultimately, the resulting clusters are analyzed based on which clusters have the highest classification probabilities. <ref>[https://docs.rapidminer.com/latest/studio/operators/modeling/segmentation/expectation_maximization_clustering.html "Expectation Maximization Clustering"] ''RapidMiner Documentation''. Retrieved April 28, 2019.</ref><br />
<br />
==== Agglomerative Hierarchical Clustering ====<br />
Also known as AGNES (Agglomerative Nesting), the Agglomerative Hierarchical Clustering Technique also creates clusters based on similarity. To start, this method takes each object and treats it as if it were a cluster. It then merges each of these clusters in pairs, until one huge cluster consisting of all the individual clusters has been formed. The result is represented in the form of a tree, which is called a ''dendrogram''. The manner in which the algorithm works is called the "bottom-up" technique. Each data entry is considered an individual element or a leaf node. At each following stage, the element is joined with its closest or most-similar element to form a bigger element or parent node. The process is repeated over and over until the root node is formed, with all of the subsequent nodes under it.<br />
<br />
The opposite of this technique is through the "top-down" method, which is implemented in an algorithm called "Divisive Clustering". This method starts at the root node, and at each iteration nodes are split or "divided" into two separate nodes, based off the ranking of dissimilarity within the clusters. This is done until every node has been divided, leaving individual clusters or leaf nodes. <ref>[https://www.datanovia.com/en/lessons/agglomerative-hierarchical-clustering/ "HIERARCHICAL CLUSTERING IN R: THE ESSENTIALS/Agglomerative Hierarchical Clustering"]. ''DataNovia''. Retrieved April 28, 2019.</ref><br />
<br />
==== Deep Learning and Neural Networks ====<br />
Neural networks are a collection of algorithms that utilize many of the concepts mentioned above while taking their capabilities a step further through deep learning. On a high level, the purpose of a neural network is to interpret raw input data through a machine perception and return patterns in the data, through techniques above such as K-means clustering or Random Forests. To do so, a neural network requires datasets to train on and thus models its interpretations on the training sets in a machine learning process. Where neural networks differ is its ability to be “stacked” to engage in deep learning. Each process is held in nodes that can be likened to neurons in a human brain. When data is encountered, many separate computations occur that can be weighted to produce the desired output. How many “layers”, or the depth, of a neural network increases its capabilities and complexity multiplicatively. <ref> A Beginner's Guide to Neural Networks and Deep Learning. (n.d.). Retrieved April 27, 2019, from https://skymind.ai/wiki/neural-network </ref><br />
<br />
== Ethical Dilemmas ==<br />
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.<br />
<br />
=== Bias ===<br />
Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. <br />
<br />
====Joy Buolamwini and Facial Recognition====<br />
Joy Buolamwini, a graduate computer science student at MIT, experienced a case of this. The facial recognition software she was working on failed to detect her face, as she had a skin tone that had not been accounted for in the facial recognition algorithm. This is because the software had used machine learning with a dataset that was not diverse enough, and as a result, the algorithm failed to recognize her.<ref>Buolamwini, Joy. [www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/ "How I'm Fighting Bias in Algorithms"]. ''MIT Media Lab''. Retrieved April 28, 2019.</ref> Safiya Noble discusses instances of algorithmic search engines reinforcing racism in her book, "Algorithms of Oppression".<ref>Noble, Safiya. ''Algorithms of Oppression''.</ref> Bias like this occurs in countless algorithms, be it through insufficient machine learning data sets, or the algorithm developers own fault, among other reasons, and it has the potential to cause legitimate problems even outside the realm of ethics.<br />
<br />
====Bias in Criminalization====<br />
[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing COMPAS] is algorithm written to determine whether a criminal is likely to re-offend using information like age, gender, and previously committed crimes. Tests have found it to be more likely to incorrectly evaluate black people than white people because it has learned on historical criminal data, which has been influenced by our biased policing practices.<ref>Larson, Mattu; Kirchner, Angwin (May 23, 2016). [https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm "How We Analyzed the COMPAS Recidivism Algorithm"]. ''Propublica''. Retrieved April 28, 2019.</ref> <br />
<br />
Jerry Kaplan is a research affiliate at Stanford University’s Center on Democracy, Development and the Rule of Law at the Freeman Spogli Institute for International Studies, where he teaches “Social and Economic Impact of Artificial Intelligence.” According to Kaplan, algorithmic bias can even influence whether or not a person is sent to jail. A 2016 study conducted by ProPublica indicated that software designed to predict the likelihood an arrestee will re-offend incorrectly flagged black defendants twice as frequently as white defendants in a decision-support system widely used by judges. Ideally, predictive systems should be wholly impartial and therefore be agnostic to skin color. However, surprisingly, the program cannot give black and white defendants who are otherwise identical the same risk score, and simultaneously match the actual recidivism rates for these two groups. This is because black defendants are re-arrested at higher rates that their white counterparts (52% versus 39%), at least in part due to racial profiling, inequities in enforcement, and harsher treatment of black people within the justice system. <ref>Kaplan, J. (December 17, 2018). [https://www.washingtonpost.com/opinions/2018/12/17/why-your-ai-might-be-racist/ "Why your AI might be racist"]. ''Washington Post''. Retrieved April 28, 2019.</ref><br />
<br />
==== Job Applicants ====<br />
Many companies employ complex algorithms in order to review and sift the thousands of resumes they will receive each year. Sometimes these algorithms display a bias which can result in people with a specific racial background or gender being recommended over others. An example of this was an Amazon AI algorithm that preferred men over women in recommending people for an interview. The algorithm employed machine learning techniques and over time taught itself to prefer men over women due to a variety of factors <ref>Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women.” Reuters, Thomson Reuters, 9 Oct. 2018, af.reuters.com/.</ref>. A major problem facing machine learning algorithms is the unpredictability in their models and what they will begin to teach themselves. It was apparent Amazon engineers did not intend for their algorithm to be bias towards men but an error resulted in this happening. Additionally, this was not a quick fix as the algorithm had been in place for years and began to pick up this bias and after analysis after a period time, it was discovered.<br />
<br />
=== Privacy And Data Gathering ===<br />
The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref>Turilli, Matteo, and Luciano Floridi (2009). "The Ethics of Information Transparency." ''Ethics and Information Technology''. '''11'''(2): 105-112. doi:10.1007/s10676-009-9187-9.</ref> is an import point regarding these issues. In popular social media algorithms, user information is often probed without the knowledge of the individual, and this can lead to problems. It is often not transparent enough how these algorithms receive user data, resulting in often incorrect information which can affect both how a person is treated within social media, as well as how outside agents could view these individuals given false data. Algorithms can also often infringe on a user’s feelings of privacy, as data can be collected that a person would prefer to be private. Data brokers are in the business of collecting peoples in formation and selling it to anyone for a profit, like data brokers companies often have their own collection of data. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref>Griffin, Andrew (October 4, 2017) [http://www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html "Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth."] ''The Independent''. Retrieved April 28, 2019.</ref> The information leaked contained data relating to usernames, passwords, as well as dates of birth. Privacy and data gathering are common ethical dilemmas relating to algorithms and are often not considered thoroughly enough by algorithm’s users.<br />
<br />
===The Filter Bubble===<br />
[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]<br />
Algorithms can be used to filter results in order to prioritize items that the user might be interested in. On some platforms, like Amazon, people can find this filtering useful because of the useful shopping recommendations the algorithm can provide. However, in other scenarios, this algorithmic filtering can become a problem. For example, Facebook has an algorithm that re-orders the user's news feed. For a period of time, the technology company prioritized sponsored posts in their algorithm. This often prioritized news articles, but there was no certainty on whether these articles came from a reliable source, simply the fact that they were sponsored. Facebook also uses its technology to gather information about its users, like which political party they belong to. This combined with prioritizing news can create a Facebook feed filled with only one party's perspective. This phenomenon is called the filter bubble, which essentially creates a platform centered completely around its user's interests. <br />
<br />
Many, like Eli Pariser, have questioned the ethical implications of the filter bubble. Pariser believes that filter bubbles are a problem because they prevent users from seeing perspectives that might challenge their own. Even worse, Pariser emphasizes that this filter bubble is invisible, meaning that the people in it do not realize that they are in it. <ref>Pariser, Eli. (2012). ''The Filter Bubble''. Penguin Books.</ref> This creates a huge lack of awareness in the world, allowing people to stand by often uninformed opinions and creating separation, instead of collaboration, with users who have different beliefs. Because of the issues Pariser outlined, Facebook decided to change their algorithm in order to prioritize posts from friends and family, in hopes of eliminating the effects of the potential filter bubble.<br />
====Filter Bubble in Politics====<br />
Another issue that these Filter Bubbles create are echo chambers; Facebook, in particular, filters out [political] content that one might disagree with, or simply not enjoy <ref>Knight, Megan (November 30, 2018). [http://theconversation.com/explainer-how-facebook-has-become-the-worlds-largest-echo-chamber-91024 "Explainer: How Facebook Has Become the World's Largest Echo Chamber"]. ''The Conversation''. Retrieved April 28, 2019.</ref>. The more a user "likes" a particular type of content, similar content will continue to appear, and perhaps content that is even more extreme. This was clearly seen in the 2016 election when without realizing it, voters developed tunnel vision. Rarely did their Facebook comfort zones expose them to opposing views, and as a result they eventually became victims to their own biases and the biases embedded within the algorithms.<ref>El-Bermawy, Mostafa M. (June 3, 2017). [https://www.wired.com/2016/11/filter-bubble-destroying-democracy/ "Your Filter Bubble Is Destroying Democracy"]. ''Wired''. Retrieved April 28, 2019.</ref> Later studies produced visualizations to show how insular the country was at the time of the election on social media and the large divide between the two echo chambers with almost no ties to each other.<ref>MIS2: Misinformation and Misbehavior Mining on the Web - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 [accessed 23 Apr, 2019]</ref> <br />
<br />
[[File:Socal-med-polarizing.png|thumbnail|right|From [https://www.researchgate.net/figure/Social-media-platforms-can-produce-echo-chambers-which-lead-to-polarization-and-can_fig4_322971747 Research Gate]: An example of algorithmic echo chambers that contribute to the polarization of political beliefs]]<br />
<br />
===Corrupt Personalization===<br />
Algorithms have the potential to become dangerous, with their most serious repercussions being the threat to democracy that is extensive personalization. Algorithms such as Facebook's are corrupt in the practice of "like recycling" that they partake in. In Christian Sandvig's article title ''Corrupt Personalization,'' he notes that Facebook has defined a "like" in two ways that the users do not realize. The first is that "anyone who clicks on a "like" button is considered to have "liked" all future content from that source," and the second is that "anyone who "likes" a comment on a shared link is considered to "like" wherever that link points to" <ref>Sandvig, Christian. “Corrupt Personalization.” Social Media Collective, 27 June 2014, socialmediacollective.org/2014/06/26/corrupt-personalization/</ref>. As a result, posts that you "like" wind up becoming ads on your friends' pages claiming that you like a certain item or thing. You are not able to see these posts and, because they do not appear on your news feed, you do not have the power to delete them. This becomes a threat to one's autonomy, for even if they wanted to delete this post, they can not. Furthermore, everyone is entitled to the ability to manage the public presentation of their own self-identity, and in this corrupt personalization, Facebook is giving users new aspects of their identity that may or may not be accurate. <br />
<br />
=== Agency And Accountability ===<br />
Algorithms make "decisions" based on the steps they were designed to follow and the input they received. This can often lead to algorithms as autonomous agents<ref>“Autonomous Agent.” Autonomous Agent - an Overview | ScienceDirect Topics, www.sciencedirect.com/topics/computer-science/autonomous-agent.</ref>, taking decision making responsibilities out of the hands of real people. Useful in terms of efficiency, these autonomous agents are capable of making decisions in a greater frequency than humans. Efficiency is what materializes the baseline for algorithm use in the first place.<br />
<br />
From an ethical standpoint, this type of agency raises many complications, specifically regarding accountability. It is no secret that many aspects of life are run by algorithms. Even events like applying to jobs are often drastically effected by these processes. Information like age, race, status, along with other qualifications, are all fed to algorithms, which then take agency and decide who moves further along in the hiring process and who is left behind. Disregarding inherent biases in this specific scenario, this process still serves to reduce the input of real humans and decrease the number of decisions that they have to make, and what is left over is the fact that autonomous agents are making systematic decisions that have extraordinary impact on people's lives. While the results of the previous example may only culminate to the occasional disregard of a qualified applicant or resentful feelings, this same principle can be much more influential.<br />
<br />
====The Trolley Problem in Practice====<br />
Consider autonomous vehicles, or self-driving cars, for instance. These are highly advanced algorithms that are programmed to make split second decisions with the greatest possible accuracy. In the case of the well-known "Trolley Problem"<ref>Roff, Heather M. “The Folly of Trolleys: Ethical Challenges and Autonomous Vehicles.” Brookings, Brookings, 17 Dec. 2018, www.brookings.edu/research/the-folly-of-trolleys-ethical-challenges-and-autonomous-vehicles/.</ref>, these agents are forced to make a decision jeopardizing one party or another. This decision can easily conclude in the injury or even death of individuals, all at the discretion of a mere program. <br />
<br />
The issue of accountability is then raised, in a situation such as this, because in the eyes of the law, society, and ethical observers, there must be someone held responsible. Attempting to prosecute a program would not be feasible in a legal situation, due to not being able to have a physical representation of the program, like you would a person. However, there are those such as Frances Grodzinsky and Kirsten Martin <ref>Martin, Kirsten. “Ethical Implications and Accountability of Algorithms.” SpringerLink, Springer Netherlands, 7 June 2018, link.springer.com/article/10.1007/s10551-018-3921-3.</ref>, who believe that the designers of an artificial agent, should be responsible for the actions of the program. <ref name="Grodzinsky"> "The ethics of designing artificial agents" by Frances S. Grodzinsky et al, Springer, 2008.</ref> Others contend this point by saying that the blame should be attributed to the users or persons directly involved in the situation. <br />
<br />
These complications will continue to arise, especially as algorithms continue to make autonomous decisions at grander scales and rates. Determining responsibility for the decisions these agents make will continue to be a vexing process, and will no doubt shape in some form many of the advanced algorithms that will be developed in the coming years.<br />
<br />
=== Intentions and Consequences ===<br />
The ethical consequences that are common in algorithm implementations can be either deliberate or unintentional. Instances where an algorithm's intent and outcome differs is noted below. <br />
<br />
==== YouTube Radicalization ====<br />
Scholar and technosociologist Zeynep Tufekci has claimed that "[[YouTube]] may be one of the most powerful radicalizing instruments of the 21st century."<ref name="YouTube Radicalization">[https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html] Tufekci, Zeynep. “YouTube, the Great Radicalizer.” The New York Times, The New York Times, 10 Mar. 2018, www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html.</ref> As YouTube algorithms aim maximize the amount of time that viewers spend watching, it inevitably discovered that the best way to do this was to show videos that slowly "up the stakes" of the subject being watched - from jogging to ultramarathons, from vegetarianism to veganism, from Trump speeches to white supremacist rants.<ref name="YouTube Radicalization" /> Thus, while the intention of Youtube is to keep viewers watching (and bring in advertisement money), they have unintentionally created a site that shows viewers more and more extreme content - contributing to radicalization. Such activity circles back to and produce filters and echo chambers.<br />
<br />
==== Facebook Advertising ====<br />
By taking a closer look at Facebook's algorithm that serves up ads to its users, gender and racial bias is obviously prominent. Using demographic and racial background as factors, Facebook's decides which ads are served up to its users. A team from Northeastern tested to see the algorithm bias in action and by running identical ads with slight tweaks to budget, images, and headings, the ads reached vastly different audiences. Results such as minorities receiving a higher percentage of low-cost housing ads, and women receiving more ads for secretary and nursing jobs <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Although the intent of Facebook may be to reach people that these ads are intended for, the companies that are signing up to advertise with Facebook have stated they did not anticipate this type of filtering when paying for their services. Additionally, although Facebook may believe it is win-win for everyone since advertisers will be getting more interactions with certain audiences targeted, and people will be happy to see ads more relatable to them shown, it is incredibly discriminatory to target people based on factors that are uncontrollable. Facebook needs to adjust its targeted advertising practices by removing racial and gender factors in their algorithms in order to prevent perpetuating stereotypes and placing people in certain boxes by race and gender. This type of algorithm goes against many ethical principles and is important that powerful technology companies are not setting poor examples for others.<br />
<br />
==See also==<br />
{{resource|<br />
*[[Bias in Information]]<br />
*[[Artificial Agents]]<br />
*[[Value Sensitive Design]]<br />
*[[Artificial Intelligence and Technology]]<br />
}}<br />
<br />
== References ==<br />
<references/><br />
[[Category:2019New]]<br />
[[Category:Concepts]]<br />
[[Category:BlueStar2019]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91428User:WikiSysop2021-02-11T16:11:42Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Zoji La Pass (India).<ref>Kelly Marks, The 12 Scariest and Most Dangerous Roads in the World. Wander Wisdom, July 25, 2020. https://wanderwisdom.com/misc/10-Most-Dangerous-Roads-in-the-World</ref><br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Frank Turner, ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91427User:WikiSysop2021-02-11T16:10:25Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Zoji La Pass (India).<ref>Kelly Marks, The 12 Scariest and Most Dangerous Roads in the World. Wander Wisdom, July 25, 2020. https://wanderwisdom.com/misc/10-Most-Dangerous-Roads-in-the-World</ref><br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
=My Frank Turner Obsession=<br />
<br />
[[File:Mavis_concert_poster.jpg|left|thumb|200px|I'll take you there!]]<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Frank Turner, ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91426User:WikiSysop2021-02-11T16:09:56Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Zoji La Pass (India).<ref>Kelly Marks, The 12 Scariest and Most Dangerous Roads in the World. Wander Wisdom, July 25, 2020. https://wanderwisdom.com/misc/10-Most-Dangerous-Roads-in-the-World</ref><br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
=My Frank Turner Obsession=<br />
<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Frank Turner, ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91425User:WikiSysop2021-02-11T15:34:08Z<p>WikiSysop: /* Me and my GPS */</p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Zoji La Pass (India).<ref>Kelly Marks, The 12 Scariest and Most Dangerous Roads in the World. Wander Wisdom, July 25, 2020. https://wanderwisdom.com/misc/10-Most-Dangerous-Roads-in-the-World</ref><br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Frank Turner, ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91424User:WikiSysop2021-02-11T15:33:27Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Joji La Pass (India).<ref>Kelly Marks, The 12 Scariest and Most Dangerous Roads in the World. Wander Wisdom, July 25, 2020. https://wanderwisdom.com/misc/10-Most-Dangerous-Roads-in-the-World</ref><br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Frank Turner, ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91423User:WikiSysop2021-02-11T15:23:52Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
<br />
<br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Turner, Frank. ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91422User:WikiSysop2021-02-11T15:22:29Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Turner, Frank. ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that Amazon ranks #93,246 in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017.</ref> <br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91421User:WikiSysop2021-02-11T15:21:23Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. With all due respect to the late, great James Brown, Frank may well be "The Hardest Working Man in Show Business.<ref>Wikipedia: James Brown. https://en.wikipedia.org/wiki/James_Brown</ref><br />
<br />
I have seen Frank Turner perform live 18 times since 2015. During lockdown in 2020, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Turner, Frank. ''The Road Beneath My Feet''. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that is ranked #93,246 by Amazon in Movies & TV.<ref> ''Get Better: A Film About Frank Turner''. Polydor, July 17, 2017</ref> <br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91420User:WikiSysop2021-02-11T15:15:44Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. I have seen him live 18 times since 2015. During 2020 lockdown, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Turner, Frank. The Road Beneath My Feet. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that is ranked #93,246 by Amazon in Movies & TV.<ref> Get Better: A Film About Frank Turner. Polydor, July 17, 2017</ref> <br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91419User:WikiSysop2021-02-11T15:14:37Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. I have seen him live 18 times since 2015. During 2020 lockdown, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Turner, Frank. The Road Beneath My Feet. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that is ranked #93,246 by Amazon in Movies & TV.<ref> Get Better: A Film About Frank Turner. Polydor, July 17, 2017. <br />
<br />
=References=<br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91418User:WikiSysop2021-02-11T15:13:58Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. I have seen him live 18 times since 2015. During 2020 lockdown, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.<br />
<br />
Frank is an erudite author.<ref>Turner, Frank. The Road Beneath My Feet. London: Abrams Press, 2016.</ref> He is also the subject of a documentary that is ranked #93,246 by Amazon in Movies & TV.<ref> Get Better: A Film About Frank Turner. Polydor, July 17, 2017. <br />
<br />
<references/></div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Topics&diff=91416Topics2021-02-09T13:46:41Z<p>WikiSysop: /* J */</p>
<hr />
<div>http://si410ethics11.projects.si.umich.edu/images/topics.png<br />
<br />
''Please add your newly created pages to this list in alphabetical order, and remember to surround with the appropriate MediaWiki syntax (i.e.:'' <nowiki>*[[your page]]</nowiki>'').''<br />
<br />
<br />
__NOTOC__<br />
<br />
<br />
== Blue Star Articles ==<br />
<br/><br />
*[[:Category:BlueStar2019|Blue Star Articles (2019)]]<br />
*[[:Category:BlueStar2018|Blue Star Articles (2018)]]<br />
*[[:Category:Blue Star|Blue Star Articles (2017)]]<br />
*[[:Category:GoldStar|Gold Star Articles (2010-2016)]]<br />
<br><br />
<br />
== List of New Articles in 2020 ==<br />
<br><br />
[[:Category:2020New|New Articles 2020]]<br />
*[[:Category:2020Concept|Concept]]<br />
*[[:Category:2020Person|Person]]<br />
*[[:Category:2020Object|Object]]<br />
<br><br />
<br />
== New Articles in 2019 ==<br />
<br><br />
[[:Category:2019New|New Articles 2019]]<br />
<br><br />
<br />
== John Walsh Thesis Revision ==<br />
<br/><br />
*[[John Walsh Thesis Revision]]<br />
<br />
== Portals and Class Writing Exercises ==<br />
<br><br />
*[[:Portal:Life on Digital Worlds|Life on Digital Worlds]]<br />
<br/><br />
<br />
== Categories ==<br />
<br><br />
{| style="width:400px;"<br />
! width="250"|Category<br />
! style="width:150px;text-align:center"|Number of Pages<br />
|-<br />
|[[:Category:Action Needed|Action Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Action Needed}}<br />
|-<br />
|[[:Category:Censorship|Censorship]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Censorship}}<br />
|-<br />
|[[:Category:Citations Needed|Citations Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Citations Needed}}<br />
|-<br />
|[[:Category:Computer Simulation|Computer Simulation]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Computer Simulation}}<br />
|-<br />
|[[:Category:Concepts|Concepts]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Concepts}}<br />
|-<br />
|[[:Category:Corporations|Corporations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Corporations}}<br />
|-<br />
|[[:Category:Cyberpunk (genre)|Cyberpunk]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Cyberpunk (genre)}}<br />
|-<br />
|[[:Category:Hardware|Hardware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Hardware}}<br />
|-<br />
|[[:Category:Information Ethics|Information Ethics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Information Ethics}}<br />
|-<br />
|[[:Category:Internet slang|Internet Slang]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Internet slang}}<br />
|-<br />
|[[:Category:Malware|Malware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Malware}}<br />
|-<br />
|[[:Category:Media Content|Media Content]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Media Content}}<br />
|-<br />
|[[:Category:Missing Information|Missing Information]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Missing Information}}<br />
|-<br />
|[[:Category:Music|Music]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Music}}<br />
|-<br />
|[[:Category:Open Source Projects|Open Source Projects]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Open Source Projects}}<br />
|-<br />
|[[:Category:Organizations|Organizations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Organizations}}<br />
|-<br />
|[[:Category:Out of Date|Out of Date]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Out of Date}}<br />
|-<br />
|[[:Category:People|People]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:People}}<br />
|-<br />
|[[:Category:Piracy|Piracy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Piracy}}<br />
|-<br />
|[[:Category:Politics|Politics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Politics}}<br />
|-<br />
|[[:Category:Portals|Portals]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Portals}}<br />
|-<br />
|[[:Category:Privacy|Privacy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Privacy}}<br />
|-<br />
|[[:Category:Services|Services]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Services}}<br />
|-<br />
|[[:Category:Social Networking|Social Networking]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Social Networking}}<br />
|-<br />
|[[:Category:Software|Software]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Software}}<br />
|-<br />
|[[:Category:Sports|Sports]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Sports}}<br />
|-<br />
|[[:Category:Video Games|Video Games]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Video Games}}<br />
|-<br />
|[[:Category:Virtual Environments, Concerns, & Issues|Virtual Environments]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Virtual Environments, Concerns, & Issues}}<br />
|-<br />
|[[:Category:Websites|Websites]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Websites}}<br />
|}<br />
<br><br />
<br />
== Topics ==<br />
<br><br />
{{Section-Menu}}<br />
{{Section|||1}}<br />
=== # ===<br />
----<br />
*[[3D printing]]<br />
*[[4chan]]<br />
*[[4shared]]<br />
*[[8tracks]]<br />
*[[9GAG]]<br />
<br />
=== A ===<br />
----<br />
*[[Aaron Swartz]]<br />
*[[Adblocking]]<br />
*[[AdverGaming]]<br />
*[[Advertising ethics online]]<br />
*[[Aging In Place Technology]]<br />
*[[Airbnb]]<br />
*[[Alex Jones]]<br />
*[[Algorithmic Justice League]]<br />
*[[Algorithms]]<br />
*[[Amazon Alexa (Amazon Echo)]]<br />
*[[Amazon.com]]<br />
*[[Ancestry data]]<br />
*''the'' [[The Amy Boyer Case|Amy Boyer Case]]<br />
*[[Android]]<br />
*[[Angry Birds]]<br />
*''Anonymous''<br />
**[[Anonymous Behavior in Virtual Environments|Behavior in Virtual Environment]]<br />
**[[Anonymous (group)|Group]]<br />
*[[Apex Legends (game)]]<br />
*[[Applicant tracking systems]]<br />
*[[Artificial Agents]]<br />
*[[Artificial Intelligence and Technology]]<br />
*[[Artificial Intelligence in China]]<br />
*[[Artificial SuperIntelligence]]<br />
*[[Ashley Madison (website)]]<br />
*[[Assassin's Creed (Main Series)]]<br />
*[[Athletes and burner accounts]]<br />
*[[Augmented Reality]]<br />
*[[Automatic gender recognition]]<br />
*[[Autonomous Systems]]<br />
*[[Autonomous Vehicles]]<br />
*[[Avatar]]<br />
<br />
=== B ===<br />
----<br />
*[[Banality of Simulated Evil]]<br />
*[[Bandcamp]]<br />
*[[Bartle Test]]<br />
*[[Battlestar Galactica (2004 TV Series)]]<br />
*[[Behavioral biometrics]]<br />
*[[Bias in Information]]<br />
*[[Biem App]]<br />
*[[Binge Watching]]<br />
*[[Biobanking]]<br />
*[[BioShock]]<br />
*[[BioWare]]<br />
*[[Bitcoins]]<br />
*[[Bitmoji]]<br />
*[[BitTorrent]]<br />
*[[Black Mirror]]<br />
*[[Black Twitter]]<br />
*[[Blizzard Entertainment]]<br />
*[[Borderlands (video game series)]]<br />
*[[Brain-Machine Interface]]<br />
*[[Brand new page]]<br />
*[[Bumble]]<br />
*[[BuzzFeed]]<br />
<br />
=== C ===<br />
----<br />
*[[Call of Duty]]<br />
*[[Cambridge Analytica]]<br />
*[[Cancel Culture]]<br />
*[[Carrier IQ]]<br />
*[[CEIU Thesis]]<br />
*[[Censorship]]<br />
*[[Censorship in China]]<br />
*[[Chatroulette]]<br />
*[[Cheating]]<br />
*[[Cheating in eSports]]<br />
*[[Cheating Technologies]]<br />
*[[Circumventing Internet Censorship]]<br />
*[[Citizendium]]<br />
*[[Civilization]]<br />
*[[Clash of Clans]]<br />
*[[Clearview AI]]<br />
*[[Click fraud]]<br />
*[[Clickbait]]<br />
*''Cloud''<br />
**[[Cloud Computing|Computing]]<br />
**[[Cloud Security|Security]]<br />
*[[Clueful Chatting]]<br />
*[[Cookies]]<br />
*[[Complex]]<br />
*[[Confidentiality of Online Data]]<br />
*[[Content moderation]]<br />
*[[Content moderation in Twitter]]<br />
*[[Content moderation in Reddit]]<br />
*[[Counter-Strike: Global Offensive (video game)]]<br />
*[[Craigslist]]<br />
*[[Creative Commons]]<br />
*[[Criminal sentencing software]]<br />
*[[Crowdsourcing]]<br />
*[[Cryptocurrency]]<br />
*''Cyber (overlaps with Online)''{{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] {{Relation|cases of|David Thorne|#D}}<br />
**[[Cybercurrency|Currency]]<br />
**[[Cyberlaw|Law]]<br />
**[[Cybersex|Sex]]<br />
***''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Cybersecurity]]<br />
**[[Cyberstalking|Stalking]] {{Relation|use of crowdsourcing|Human Flesh Search|#H}} {{Relation||Tiayna.cn|#T}} {{Relation|cases of|Amy Boyer|#A}}<br />
**[[Cyberwarfare|Warfare]]<br />
*[[Cybersecurity in Banking]]<br />
<br />
=== D ===<br />
----<br />
*[[Daily Fantasy Sports]]<br />
*[[Dark Patterns]]<br />
*[[Dark Web]]<br />
*''Data (overlaps with Information)''<br />
**[[Data Aggregation Online|Aggregation Online]]<br />
**[[Data Mining|Mining]]<br />
*[[Deontology]] <br />
*[[Data brokers]]<br />
*[[Datafication of Children]]<br />
*[[Dating Apps]]<br />
*[[David Thorne]]<br />
*[[Da Vinci Surgical System]]<br />
*[[Deepfake]]<br />
*[[Defcon (video game)]]<br />
*[[Detroit: Become Human]]<br />
*[[Deus Ex (Series)]]<br />
**[[Deus Ex: Human Revolution]]<br />
*[[Device implant]]<br />
*[[Diablo (Franchise)]]<br />
**[[Diablo II]]<br />
**[[Diablo III]]<br />
*[[Diaspora]]<br />
*''Digital''<br />
**[[Digital Property|Property]]<br />
*[[Digital_divide]]<br />
**[[Digital DJing|DJing]]<br />
**[[Digital Piracy|Piracy]]<br />
**[[Digital Rights Management|Rights Management]]<br />
*[[Disclosive Ethics]]<br />
*[[DoorDash]]<br />
*[[DNA Testing]]<br />
*[[Domain Name System]]<br />
*[[Downloadable Content in Video Games]]<br />
*[[Dragonfly]]<br />
*[[Drones]]<br />
*[[Dropbox]]<br />
*[[Drupal]]<br />
*[[Duke F*** List]]<br />
<br />
=== E ===<br />
----<br />
*[[eBay]]<br />
*[[Edward Castronova]]<br />
*[[Edward H. Spence]]<br />
*[[Edward Snowden]]<br />
*[[Elder Scrolls]]<br />
*[[Electronic Arts]]<br />
*[[Electric Sheep]]<br />
*[[Electronic voting systems]]<br />
*''Electronic''<br />
**[[Electronic Health Records|Health Records]]<br />
**[[Electronic Sports|Sports]]<br />
*[[Elizabeth Holmes]]<br />
*[[Elon Musk]]<br />
*[[Empathy in Gaming]]<br />
*[[Emoji]]<br />
*[[Employers and Online Privacy]]<br />
*[[The Entire History of You]]<br />
*''Ethics''<br />
**''and'' [[Data Equity]]<br />
**''in'' [[Ethics in Computer & Video Games|Computer & Video Games]]<br />
**''in'' [[Ethics in Hacking|Hacking]]<br />
**''of'' [[Information Ethics|Information]]<br />
*[[Ethical game design]]<br />
*[[Etsy]]<br />
*[[Experience Project]]<br />
<br />
=== F ===<br />
----<br />
*''Facebook''<br />
**[[Advertising on Facebook]]<br />
**[[Facebook|Company]]<br />
**[[Facebook Messenger]]<br />
**[[Facebook newsfeed curation]]<br />
**[[Facebook Privacy Policy|Privacy Policy]]<br />
**[[Data Mining and Manipulation]]<br />
**[[Facebook in Africa]]<br />
*[[FaceTime]]<br />
*[[Face recognition]]<br />
*[[Face recognition in law enforcement]]<br />
*[[Fake News]]<br />
*[[Fan fiction]]<br />
*[[Find My Friends]]<br />
*[[File Sharing]]<br />
*[[Filter Bubble]]<br />
*[[Final Fantasy XIV]]<br />
*[[Fitness Game]]<br />
*[[First Person Shooters]]<br />
*[[Flaming]]<br />
*[[Free Basics]]<br />
*[[Freedom of Expression]]<br />
*[[Freemium model]]<br />
<br />
=== G ===<br />
----<br />
*[[Galaxy S3]]<br />
*[[Game Addiction]]<br />
*[[Gamergate]]<br />
*[[Gattaca]]<br />
*[[Gender bias in Wikipedia]]<br />
*[[Gender in Video Games]]<br />
*[[Genealogy platforms]]<br />
*[[General Data Protection Regulation]]<br />
*[[Genetically Modified Food]]<br />
*[[Gene Editing]]<br />
*[[Genomics]]<br />
*[[Genovese Syndrome]]<br />
*[[Geographic Information Systems]]<br />
*[[George Hotz]]<br />
*[[Ghost Writing Online]]<br />
*[[Girls Around Me]]<br />
*[[GLANSER]]<br />
*''Google''<br />
**[[Google|Company]]<br />
**[[Google Books|Books]]<br />
**[[Google Glass| Google Glass]]<br />
**[[Google Home]]<br />
**[[Google Clips]]<br />
**[[Google Street View|Street View]]<br />
*[[Goohah]]<br />
*[[Grand Theft Auto IV]]<br />
*[[Grand Theft Auto V]]<br />
*[[Griefing]]<br />
*[[Grindr]]<br />
<br />
=== H ===<br />
----<br />
*[[Hackers]]<br />
*[[Hacking the 2016 US Presidential Election]]<br />
*[[Health Informatics]]<br />
*[[Her (film) (2013)]]<br />
*[[Her Interactive]]<br />
*[[Herman Tavani]]<br />
*[[High Frequency Trading]]<br />
*[[Hulu]]<br />
*[[Human Flesh Search]] {{Relation|related to|Tianya.cn|#T}}<br />
*[[Human Microchipping]]<br />
*[["Human out of the Loop" Military Systems]]<br />
*[[Humans (British TV Series)]]<br />
<br />
=== I ===<br />
----<br />
*[[iCloud]]<br />
*[[id Software]]<br />
*[[Imgur]]<br />
*[[Infamous (series)]]<br />
*[[Influencer Marketing]]<br />
*[[Infoglut]]<br />
*[[Informatics]]<br />
*''Information'' {{Relation|overlaps with|Data|#Data}}<br />
**[[Information Ethics|Ethics]]<br />
**[[Information Freedom|Freedom]]<br />
**[[Freedom_of_Information_policies|Freedom of Information Policy]] <br />
**[[Information Overload|Overload]]<br />
**[[Information Reliability|Reliability]]<br />
**[[Information Security|Security]]<br />
**[[Information Transparency|Transparency]]<br />
**[[Information Vandalism|Vandalism]]<br />
*[[Informational Friction]]<br />
*[[Infosphere]]<br />
*[[Instagram]]<br />
*[[Instagram Ads]]<br />
*[[Intellectual Property]]<br />
*[[Internet of things]]<br />
*''Internet'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Online|#Online}} {{Relation||Virtual|#Virtual}}<br />
**[[Internet Archive|Archive]]<br />
**[[Internet Censorship in Hong Kong|Censorship in Hong Kong]]<br />
**[[Internet Censorship in South Korea|Censorship in South Korea]]<br />
**[[Internet Censorship in the United Kingdom|Censorship in the United Kingdom]]<br />
**[[Cybersecurity Law in Vietnam|Censorship in Vietnam]]<br />
**''in'' [[Circumventing Internet Censorship|Circumventing Censorship]]<br />
**[[Internet Control|Control]]<br />
**[[Internet meme|Meme]]<br />
*[[Iris Recognition]]<br />
<br />
=== J ===<br />
----<br />
*[[Jack Dorsey]]<br />
*[[Jailbreaking]]<br />
*[[James H. Moor]]<br />
*[[Jeremy Bentham]]<br />
*[[John Weckert]]<br />
*[[Julian Dibbell]]<br />
*[[Just another mediawiki page]]<br />
<br />
=== K ===<br />
----<br />
*[[Kathleen Wallace]]<br />
*[[Kay Mathiesen]]<br />
*[[Kim Dotcom]]<br />
*[[Kickstarter]]<br />
*[[Kind of Bloop]]<br />
<br />
=== L ===<br />
----<br />
*[[LambdaMOO]]<br />
*[[Larry Ellison]]<br />
*[[Lawrence Lessig]]<br />
*[[League of Legends]]<br />
*[[The League (Dating App)]]<br />
*[[LikeALittle]]<br />
*[[Limewire]]<br />
*[[Line (Application)]]<br />
*[[LinkedIn]]<br />
*[[Linus Torvalds]]<br />
*[[Live Video]]<br />
*[[Location targeted advertising]]<br />
*[[Lookbook.nu]]<br />
*[[Loot Box]]<br />
*[[Love Plus]]<br />
*[[Low Orbit Ion Cannon]]<br />
*[[Luciano Floridi]]<br />
*[[Lyft]]<br />
<br />
=== M ===<br />
----<br />
*[[Machine learning in healthcare]]<br />
*[[macOS]]<br />
*[[Manhunt]]<br />
*[[MapleStory]]<br />
*[[Mark Zuckerberg]]<br />
*[[Mashup]]<br />
*[[Mass Effect]]<br />
*''the'' [[The Matrix|Matrix]]<br />
*[[Internet meme|Meme]]<br />
*[[Mechanical Turk]]<br />
*[[Megaupload]]<br />
*[[Mia Consalvo]]<br />
*[[Microsoft chatbots]]<br />
*[[Microtransactions]]<br />
*[[Miguel Sicart]]<br />
*[[Military Entertainment Complex]]<br />
*[[Minecraft]]<br />
*[[Mirai Botnet]]<br />
*[[Misinformation]]<br />
*[[MMORPGs]]<br />
*[[Mods]]<br />
*[[MOOC (Massive Open Online Courses)]]<br />
*[[Moore's Law]]<br />
*[[Morris Worm]]<br />
*[[Mortal Kombat]]<br />
*[[Mr. Robot]]<br />
*[[Music piracy]]<br />
*[[Myspace]]<br />
<br />
=== N ===<br />
----<br />
*[[Napster]]<br />
*[[National Security Agency]]<br />
*[[NSA Cryptography]]<br />
*[[NCAA Football (Video Game Series)]]<br />
*[[Need For Speed (Video Game Series)]]<br />
*[[Nest Thermostat]]<br />
*[[Net neutrality]]<br />
*[[Netflix]]<br />
*[[Nextdoor]]<br />
*[[Norbert Wiener]]<br />
*[[Nosedive, Black Mirror]]<br />
*[[Nymwars]]<br />
<br />
=== O ===<br />
----<br />
*[[OK The Pirate Bay]]<br />
*[[Omegle]]<br />
*''Online'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] ''in Cyberspace''<br />
**[[Online Cheating|Cheating]]<br />
**[[Online Dating|Dating]]<br />
**[[Online Gambling|Gambling]]<br />
**[[Online Identity|Identity]]<br />
**[[Online Identity Theft|Identity Theft]]<br />
**[[Libel Online|Libel]]<br />
**[[Online Reputation Systems|Reputation Systems]]<br />
**''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Online shopping|Shopping]]<br />
**[[Cyberstalking|Stalking]] ''in CyberSpace''<br />
*[[Old School Runescape]]<br />
*[[The Open Internet|Open Internet]]<br />
*[[Onavo]]<br />
*[[OnStar]]<br />
*[[OpenAi]]<br />
*[[Orwell (Video Game)]]<br />
*[[Overwatch]]<br />
*[[Open Source Software]]<br />
<br />
=== P ===<br />
----<br />
*[[Palantir Technologies]]<br />
*[[Pandora]]<br />
*[[Parasocial Relationship]]<br />
*[[PARO Therapeutic Robot]]<br />
*[[Parody]]<br />
*[[Patents]]<br />
*[[PayPal]]<br />
*[[Periscope]]<br />
*[[Peter Thiel]]<br />
*[[Philip Brey]]<br />
*''Photo'' {{Relation|issues of|Digital Photography|#D}}<br />
**[[Photo Editing|Editing]]<br />
**[[Phototruth|Truth]]<br />
*[[Pinterest]]<br />
*[[The Pirate Bay|Pirate Bay]]<br />
*[[Plagiarism]]<br />
*[[PlayerUnknown's Battlegrounds]]<br />
*[[Pokémon Go]]<br />
*[[PokerStars]]<br />
*[[Pornography]]<br />
*[[Portal 2]]<br />
*[[Postal 2]]<br />
*[[Prank YouTubers]]<br />
*''Privacy''<br />
**[[Facebook Privacy Policy|Facebook Policy]]<br />
**''in'' [[Privacy in the China|China]]<br />
**''in'' [[Privacy in the Online Environment|Online Enviornment]]<br />
**''in'' [[Privacy in Social Networking|Social Networking]]<br />
**''in'' [[Privacy in public]]<br />
**[[Privacy Policies at Apple Inc.|Apple Policy]]<br />
*[[Privacy in Venmo]]<br />
*[[Privacy in the China]]<br />
*[[Pro-Ana Forums]]<br />
*[[Protect IP Act]]<br />
*[[Proxy Culture]]<br />
*[[Public Morality]]<br />
*[[The Punisher]]<br />
*[[Punishments in Virtual Environments]]<br />
<br />
===Q ===<br />
-----<br />
*[[Quora]]<br />
=== R ===<br />
----<br />
*[[Racial Algorithmic Bias]]<br />
*[[Racism in Video Games]]<br />
*[[Radio-frequency Identification]]<br />
*[[Ransomware]]<br />
*[[Raph Koster]]<br />
*[[Ray Kurzweil]]<br />
*[[Real Fake Page]]<br />
*[[Real Money Trade]]<br />
*[[Recommender Systems]]<br />
*[[Reddit]]<br />
**[[/r/AmITheAsshole]]<br />
**[[/r/wallstreetbets]]<br />
**[[/r/2meirl4meirl]]<br />
*[[Reid Hoffman]]<br />
*[[Renren]]<br />
*[[Richard Stallman]]<br />
*[[Right to be Forgotten]]<br />
*[[RIP Trolling]]<br />
*[[Rockmelt]]<br />
<br />
=== S ===<br />
------<br />
*[[Sampling (hip hop)]]<br />
*[[Self Driving Cars]]<br />
*[[Sergey Aleynikov]]<br />
*[[Serious Games]]<br />
*[[Sexting]]<br />
*[[Sharing Subscription Services]]<br />
*''Sims''<br />
**[[The Sims 3|The Sims 3]]<br />
**[[The Sims Online|The Sims Online]]<br />
**[[The Sims 4|The Sims 4]]<br />
*[[Slack (Application)]]<br />
*[[Smart Doorbell]]<br />
*[[Smart Home]]<br />
*[[Smartphones (Location Services)]]<br />
*[[Soccer & FIFA]]<br />
*[[Social Credit System]]<br />
*''Social''<br />
**[[Social Media in Sports|Media in Sports]]<br />
**[[Social media in national elections (2016)]]<br />
**[[Social Networking|Networking]]<br />
**[[Social Networking Services|Networking Services]] {{Relation|for sites|Facebook|Facebook}} {{Relation||Tianya.cn|Tianya.cn}} {{Relation||Twitter|Twitter}} {{Relation||Tumblr|Tumblr}}<br />
**[[Social Media (Meta)|Media (Meta)]]<br />
*[[Social media and the 2020 US presidential election]]<br />
*[[Social Media Websites in Investigations]]<br />
* [[Sousveillance]]<br />
*[[Snapchat]]<br />
*[[Spam]]<br />
*[[Spoof]]<br />
*[[Spotify]]<br />
*[[Spycams in South Korea]]<br />
*[[Starcraft II]]<br />
*[[Statistical Modeling]]<br />
*[[Steam]]<br />
*[[Steve Jobs]]<br />
*[[Stop Online Piracy Act]]<br />
*[[Student-Athlete Social Media Monitoring]]<br />
*[[StumbleUpon]]<br />
*[[Stuxnet Trojan]] {{Relation|type of|Worm|#W}} {{Relation|utilizes|Rootkit|#R}}<br />
*[[Surveillance Capitalism]]<br />
*[[Surveillance in China]]<br />
*[[Surveillance Technologies]]<br />
*[[Sword Art Online]]<br />
<br />
=== T ===<br />
------<br />
*[[Targeted Advertising (Online)]]<br />
*[[Team Fortress 2]]<br />
*[[Technological Determinism]]<br />
*[[Technological Singularity]]<br />
*[[Telepresence]]<br />
*[[Tencent]]<br />
*[[Tesla, Inc.]]<br />
*[[Testimonials]]<br />
*[[The Truman Show]]<br />
*[[Thomas M. Powers]]<br />
*[[Tianya.cn]]<br />
*[[TikTok]]<br />
*[[Tim Berners-Lee]]<br />
*[[Tinder]]<br />
*[[Tor]]<br />
*[[Transhumanism]]<br />
*[[Transparency in software development]]<br />
*[[Trustworthiness of information]]<br />
*[[Troll]]<br />
*[[Tumblr]]<br />
*[[Twitter]]<br />
*[[Twitch.tv]]<br />
<br />
=== U ===<br />
----<br />
*[[Uber]]<br />
*[[Ubiquitous Computing]]<br />
*[[Unabomber Manifesto]]<br />
*[[Uniqueness Debate]]<br />
*[[Undertale]]<br />
*[[Utilitarian Philosophy]]<br />
<br />
=== V ===<br />
----<br />
*[[Valve]]<br />
*[[Value Sensitive Design]]<br />
*[[Venmo]]<br />
*''Virtual'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Online|#Online}}<br />
**[[Virtual Assistants]]<br />
**''Bullying in'' [[Cyberbullying|Cyberspace]]<br />
**[[Virtual Child Pornography|Child Pornography]]<br />
**[[Virtual Community|Community]]<br />
**[[Virtual Crimes and Punishments|Crimes and Punishments]]<br />
** ''Dating ''[[Online Dating#Virtual_Dating|Online]]<br />
**[[Virtual Dating Simulations|Dating Simulations]]<br />
**[[Virtual Environment|Environment]]<br />
**[[Punishments in Virtual Environments|Punishment]]<br />
**[[Virtual Rape|Rape]]<br />
**''Sex in'' [[Cybersex|Cyberspace]]<br />
**''Stalking in'' [[Cyberstalking|Cyberspace]]<br />
*[[Virtual sweatshops]]<br />
*[[Violence and video games]]<br />
*[[Violence in Video Games]]<br />
*[[Virtual Magic Kingdom]]<br />
*[[Virtual Reality and Computer Simulations]]<br />
*[[Virtual Reality in Prison]]<br />
*[[Video Surveillance]]<br />
*[[Voice imitation algorithms]]<br />
*[[Vlogging]]<br />
*[[Vuze]]<br />
<br />
=== W ===<br />
----<br />
*[[Warcraft III]]<br />
*[[Watch Dogs]]<br />
*[[Watson (computer)]]<br />
*[[Wattpad]]<br />
*[[Waze]]<br />
*[[Wearable health tech]]<br />
*[[Web 2.0]]<br />
*[[Webcams]]<br />
*[[Webtoon App]]<br />
*[[WeChat]]<br />
*[[Weibo]]<br />
*[[Westworld and AI]]<br />
*[[WhatsApp]]<br />
*[[Whisper]]<br />
*[[Wii U]]<br />
*[[WikiLeaks]]<br />
*[[Wikipedia]]<br />
**[[Wikipedia Bots|Bots]]<br />
**[[Gender bias in Wikipedia]]<br />
*[[Witcher 3: Wild Hunt]]<br />
*[[Women in Gaming]]<br />
*[[World of Warcraft]]<br />
<br />
=== X ===<br />
----<br />
*[[The X-Files]]<br />
*[[Xkcd]]<br />
<br />
<br />
=== Y ===<br />
----<br />
*[[Yelp Reviewing]]<br />
*[[Yik Yak]]<br />
*''YouTube''<br />
**[[YouTube|YouTube (Website)]]<br />
**[[YouTube Beauty Community|Beauty Community]]<br />
**[[YouTube recommendation algorithm]]<br />
<br />
=== Z ===<br />
----<br />
*[[Zynga]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Topics&diff=91415Topics2021-02-09T13:45:29Z<p>WikiSysop: /* B */</p>
<hr />
<div>http://si410ethics11.projects.si.umich.edu/images/topics.png<br />
<br />
''Please add your newly created pages to this list in alphabetical order, and remember to surround with the appropriate MediaWiki syntax (i.e.:'' <nowiki>*[[your page]]</nowiki>'').''<br />
<br />
<br />
__NOTOC__<br />
<br />
<br />
== Blue Star Articles ==<br />
<br/><br />
*[[:Category:BlueStar2019|Blue Star Articles (2019)]]<br />
*[[:Category:BlueStar2018|Blue Star Articles (2018)]]<br />
*[[:Category:Blue Star|Blue Star Articles (2017)]]<br />
*[[:Category:GoldStar|Gold Star Articles (2010-2016)]]<br />
<br><br />
<br />
== List of New Articles in 2020 ==<br />
<br><br />
[[:Category:2020New|New Articles 2020]]<br />
*[[:Category:2020Concept|Concept]]<br />
*[[:Category:2020Person|Person]]<br />
*[[:Category:2020Object|Object]]<br />
<br><br />
<br />
== New Articles in 2019 ==<br />
<br><br />
[[:Category:2019New|New Articles 2019]]<br />
<br><br />
<br />
== John Walsh Thesis Revision ==<br />
<br/><br />
*[[John Walsh Thesis Revision]]<br />
<br />
== Portals and Class Writing Exercises ==<br />
<br><br />
*[[:Portal:Life on Digital Worlds|Life on Digital Worlds]]<br />
<br/><br />
<br />
== Categories ==<br />
<br><br />
{| style="width:400px;"<br />
! width="250"|Category<br />
! style="width:150px;text-align:center"|Number of Pages<br />
|-<br />
|[[:Category:Action Needed|Action Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Action Needed}}<br />
|-<br />
|[[:Category:Censorship|Censorship]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Censorship}}<br />
|-<br />
|[[:Category:Citations Needed|Citations Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Citations Needed}}<br />
|-<br />
|[[:Category:Computer Simulation|Computer Simulation]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Computer Simulation}}<br />
|-<br />
|[[:Category:Concepts|Concepts]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Concepts}}<br />
|-<br />
|[[:Category:Corporations|Corporations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Corporations}}<br />
|-<br />
|[[:Category:Cyberpunk (genre)|Cyberpunk]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Cyberpunk (genre)}}<br />
|-<br />
|[[:Category:Hardware|Hardware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Hardware}}<br />
|-<br />
|[[:Category:Information Ethics|Information Ethics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Information Ethics}}<br />
|-<br />
|[[:Category:Internet slang|Internet Slang]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Internet slang}}<br />
|-<br />
|[[:Category:Malware|Malware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Malware}}<br />
|-<br />
|[[:Category:Media Content|Media Content]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Media Content}}<br />
|-<br />
|[[:Category:Missing Information|Missing Information]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Missing Information}}<br />
|-<br />
|[[:Category:Music|Music]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Music}}<br />
|-<br />
|[[:Category:Open Source Projects|Open Source Projects]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Open Source Projects}}<br />
|-<br />
|[[:Category:Organizations|Organizations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Organizations}}<br />
|-<br />
|[[:Category:Out of Date|Out of Date]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Out of Date}}<br />
|-<br />
|[[:Category:People|People]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:People}}<br />
|-<br />
|[[:Category:Piracy|Piracy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Piracy}}<br />
|-<br />
|[[:Category:Politics|Politics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Politics}}<br />
|-<br />
|[[:Category:Portals|Portals]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Portals}}<br />
|-<br />
|[[:Category:Privacy|Privacy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Privacy}}<br />
|-<br />
|[[:Category:Services|Services]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Services}}<br />
|-<br />
|[[:Category:Social Networking|Social Networking]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Social Networking}}<br />
|-<br />
|[[:Category:Software|Software]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Software}}<br />
|-<br />
|[[:Category:Sports|Sports]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Sports}}<br />
|-<br />
|[[:Category:Video Games|Video Games]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Video Games}}<br />
|-<br />
|[[:Category:Virtual Environments, Concerns, & Issues|Virtual Environments]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Virtual Environments, Concerns, & Issues}}<br />
|-<br />
|[[:Category:Websites|Websites]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Websites}}<br />
|}<br />
<br><br />
<br />
== Topics ==<br />
<br><br />
{{Section-Menu}}<br />
{{Section|||1}}<br />
=== # ===<br />
----<br />
*[[3D printing]]<br />
*[[4chan]]<br />
*[[4shared]]<br />
*[[8tracks]]<br />
*[[9GAG]]<br />
<br />
=== A ===<br />
----<br />
*[[Aaron Swartz]]<br />
*[[Adblocking]]<br />
*[[AdverGaming]]<br />
*[[Advertising ethics online]]<br />
*[[Aging In Place Technology]]<br />
*[[Airbnb]]<br />
*[[Alex Jones]]<br />
*[[Algorithmic Justice League]]<br />
*[[Algorithms]]<br />
*[[Amazon Alexa (Amazon Echo)]]<br />
*[[Amazon.com]]<br />
*[[Ancestry data]]<br />
*''the'' [[The Amy Boyer Case|Amy Boyer Case]]<br />
*[[Android]]<br />
*[[Angry Birds]]<br />
*''Anonymous''<br />
**[[Anonymous Behavior in Virtual Environments|Behavior in Virtual Environment]]<br />
**[[Anonymous (group)|Group]]<br />
*[[Apex Legends (game)]]<br />
*[[Applicant tracking systems]]<br />
*[[Artificial Agents]]<br />
*[[Artificial Intelligence and Technology]]<br />
*[[Artificial Intelligence in China]]<br />
*[[Artificial SuperIntelligence]]<br />
*[[Ashley Madison (website)]]<br />
*[[Assassin's Creed (Main Series)]]<br />
*[[Athletes and burner accounts]]<br />
*[[Augmented Reality]]<br />
*[[Automatic gender recognition]]<br />
*[[Autonomous Systems]]<br />
*[[Autonomous Vehicles]]<br />
*[[Avatar]]<br />
<br />
=== B ===<br />
----<br />
*[[Banality of Simulated Evil]]<br />
*[[Bandcamp]]<br />
*[[Bartle Test]]<br />
*[[Battlestar Galactica (2004 TV Series)]]<br />
*[[Behavioral biometrics]]<br />
*[[Bias in Information]]<br />
*[[Biem App]]<br />
*[[Binge Watching]]<br />
*[[Biobanking]]<br />
*[[BioShock]]<br />
*[[BioWare]]<br />
*[[Bitcoins]]<br />
*[[Bitmoji]]<br />
*[[BitTorrent]]<br />
*[[Black Mirror]]<br />
*[[Black Twitter]]<br />
*[[Blizzard Entertainment]]<br />
*[[Borderlands (video game series)]]<br />
*[[Brain-Machine Interface]]<br />
*[[Brand new page]]<br />
*[[Bumble]]<br />
*[[BuzzFeed]]<br />
<br />
=== C ===<br />
----<br />
*[[Call of Duty]]<br />
*[[Cambridge Analytica]]<br />
*[[Cancel Culture]]<br />
*[[Carrier IQ]]<br />
*[[CEIU Thesis]]<br />
*[[Censorship]]<br />
*[[Censorship in China]]<br />
*[[Chatroulette]]<br />
*[[Cheating]]<br />
*[[Cheating in eSports]]<br />
*[[Cheating Technologies]]<br />
*[[Circumventing Internet Censorship]]<br />
*[[Citizendium]]<br />
*[[Civilization]]<br />
*[[Clash of Clans]]<br />
*[[Clearview AI]]<br />
*[[Click fraud]]<br />
*[[Clickbait]]<br />
*''Cloud''<br />
**[[Cloud Computing|Computing]]<br />
**[[Cloud Security|Security]]<br />
*[[Clueful Chatting]]<br />
*[[Cookies]]<br />
*[[Complex]]<br />
*[[Confidentiality of Online Data]]<br />
*[[Content moderation]]<br />
*[[Content moderation in Twitter]]<br />
*[[Content moderation in Reddit]]<br />
*[[Counter-Strike: Global Offensive (video game)]]<br />
*[[Craigslist]]<br />
*[[Creative Commons]]<br />
*[[Criminal sentencing software]]<br />
*[[Crowdsourcing]]<br />
*[[Cryptocurrency]]<br />
*''Cyber (overlaps with Online)''{{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] {{Relation|cases of|David Thorne|#D}}<br />
**[[Cybercurrency|Currency]]<br />
**[[Cyberlaw|Law]]<br />
**[[Cybersex|Sex]]<br />
***''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Cybersecurity]]<br />
**[[Cyberstalking|Stalking]] {{Relation|use of crowdsourcing|Human Flesh Search|#H}} {{Relation||Tiayna.cn|#T}} {{Relation|cases of|Amy Boyer|#A}}<br />
**[[Cyberwarfare|Warfare]]<br />
*[[Cybersecurity in Banking]]<br />
<br />
=== D ===<br />
----<br />
*[[Daily Fantasy Sports]]<br />
*[[Dark Patterns]]<br />
*[[Dark Web]]<br />
*''Data (overlaps with Information)''<br />
**[[Data Aggregation Online|Aggregation Online]]<br />
**[[Data Mining|Mining]]<br />
*[[Deontology]] <br />
*[[Data brokers]]<br />
*[[Datafication of Children]]<br />
*[[Dating Apps]]<br />
*[[David Thorne]]<br />
*[[Da Vinci Surgical System]]<br />
*[[Deepfake]]<br />
*[[Defcon (video game)]]<br />
*[[Detroit: Become Human]]<br />
*[[Deus Ex (Series)]]<br />
**[[Deus Ex: Human Revolution]]<br />
*[[Device implant]]<br />
*[[Diablo (Franchise)]]<br />
**[[Diablo II]]<br />
**[[Diablo III]]<br />
*[[Diaspora]]<br />
*''Digital''<br />
**[[Digital Property|Property]]<br />
*[[Digital_divide]]<br />
**[[Digital DJing|DJing]]<br />
**[[Digital Piracy|Piracy]]<br />
**[[Digital Rights Management|Rights Management]]<br />
*[[Disclosive Ethics]]<br />
*[[DoorDash]]<br />
*[[DNA Testing]]<br />
*[[Domain Name System]]<br />
*[[Downloadable Content in Video Games]]<br />
*[[Dragonfly]]<br />
*[[Drones]]<br />
*[[Dropbox]]<br />
*[[Drupal]]<br />
*[[Duke F*** List]]<br />
<br />
=== E ===<br />
----<br />
*[[eBay]]<br />
*[[Edward Castronova]]<br />
*[[Edward H. Spence]]<br />
*[[Edward Snowden]]<br />
*[[Elder Scrolls]]<br />
*[[Electronic Arts]]<br />
*[[Electric Sheep]]<br />
*[[Electronic voting systems]]<br />
*''Electronic''<br />
**[[Electronic Health Records|Health Records]]<br />
**[[Electronic Sports|Sports]]<br />
*[[Elizabeth Holmes]]<br />
*[[Elon Musk]]<br />
*[[Empathy in Gaming]]<br />
*[[Emoji]]<br />
*[[Employers and Online Privacy]]<br />
*[[The Entire History of You]]<br />
*''Ethics''<br />
**''and'' [[Data Equity]]<br />
**''in'' [[Ethics in Computer & Video Games|Computer & Video Games]]<br />
**''in'' [[Ethics in Hacking|Hacking]]<br />
**''of'' [[Information Ethics|Information]]<br />
*[[Ethical game design]]<br />
*[[Etsy]]<br />
*[[Experience Project]]<br />
<br />
=== F ===<br />
----<br />
*''Facebook''<br />
**[[Advertising on Facebook]]<br />
**[[Facebook|Company]]<br />
**[[Facebook Messenger]]<br />
**[[Facebook newsfeed curation]]<br />
**[[Facebook Privacy Policy|Privacy Policy]]<br />
**[[Data Mining and Manipulation]]<br />
**[[Facebook in Africa]]<br />
*[[FaceTime]]<br />
*[[Face recognition]]<br />
*[[Face recognition in law enforcement]]<br />
*[[Fake News]]<br />
*[[Fan fiction]]<br />
*[[Find My Friends]]<br />
*[[File Sharing]]<br />
*[[Filter Bubble]]<br />
*[[Final Fantasy XIV]]<br />
*[[Fitness Game]]<br />
*[[First Person Shooters]]<br />
*[[Flaming]]<br />
*[[Free Basics]]<br />
*[[Freedom of Expression]]<br />
*[[Freemium model]]<br />
<br />
=== G ===<br />
----<br />
*[[Galaxy S3]]<br />
*[[Game Addiction]]<br />
*[[Gamergate]]<br />
*[[Gattaca]]<br />
*[[Gender bias in Wikipedia]]<br />
*[[Gender in Video Games]]<br />
*[[Genealogy platforms]]<br />
*[[General Data Protection Regulation]]<br />
*[[Genetically Modified Food]]<br />
*[[Gene Editing]]<br />
*[[Genomics]]<br />
*[[Genovese Syndrome]]<br />
*[[Geographic Information Systems]]<br />
*[[George Hotz]]<br />
*[[Ghost Writing Online]]<br />
*[[Girls Around Me]]<br />
*[[GLANSER]]<br />
*''Google''<br />
**[[Google|Company]]<br />
**[[Google Books|Books]]<br />
**[[Google Glass| Google Glass]]<br />
**[[Google Home]]<br />
**[[Google Clips]]<br />
**[[Google Street View|Street View]]<br />
*[[Goohah]]<br />
*[[Grand Theft Auto IV]]<br />
*[[Grand Theft Auto V]]<br />
*[[Griefing]]<br />
*[[Grindr]]<br />
<br />
=== H ===<br />
----<br />
*[[Hackers]]<br />
*[[Hacking the 2016 US Presidential Election]]<br />
*[[Health Informatics]]<br />
*[[Her (film) (2013)]]<br />
*[[Her Interactive]]<br />
*[[Herman Tavani]]<br />
*[[High Frequency Trading]]<br />
*[[Hulu]]<br />
*[[Human Flesh Search]] {{Relation|related to|Tianya.cn|#T}}<br />
*[[Human Microchipping]]<br />
*[["Human out of the Loop" Military Systems]]<br />
*[[Humans (British TV Series)]]<br />
<br />
=== I ===<br />
----<br />
*[[iCloud]]<br />
*[[id Software]]<br />
*[[Imgur]]<br />
*[[Infamous (series)]]<br />
*[[Influencer Marketing]]<br />
*[[Infoglut]]<br />
*[[Informatics]]<br />
*''Information'' {{Relation|overlaps with|Data|#Data}}<br />
**[[Information Ethics|Ethics]]<br />
**[[Information Freedom|Freedom]]<br />
**[[Freedom_of_Information_policies|Freedom of Information Policy]] <br />
**[[Information Overload|Overload]]<br />
**[[Information Reliability|Reliability]]<br />
**[[Information Security|Security]]<br />
**[[Information Transparency|Transparency]]<br />
**[[Information Vandalism|Vandalism]]<br />
*[[Informational Friction]]<br />
*[[Infosphere]]<br />
*[[Instagram]]<br />
*[[Instagram Ads]]<br />
*[[Intellectual Property]]<br />
*[[Internet of things]]<br />
*''Internet'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Online|#Online}} {{Relation||Virtual|#Virtual}}<br />
**[[Internet Archive|Archive]]<br />
**[[Internet Censorship in Hong Kong|Censorship in Hong Kong]]<br />
**[[Internet Censorship in South Korea|Censorship in South Korea]]<br />
**[[Internet Censorship in the United Kingdom|Censorship in the United Kingdom]]<br />
**[[Cybersecurity Law in Vietnam|Censorship in Vietnam]]<br />
**''in'' [[Circumventing Internet Censorship|Circumventing Censorship]]<br />
**[[Internet Control|Control]]<br />
**[[Internet meme|Meme]]<br />
*[[Iris Recognition]]<br />
<br />
=== J ===<br />
----<br />
*[[Jack Dorsey]]<br />
*[[Jailbreaking]]<br />
*[[James H. Moor]]<br />
*[[Jeremy Bentham]]<br />
*[[John Weckert]]<br />
*[[Julian Dibbell]]<br />
<br />
=== K ===<br />
----<br />
*[[Kathleen Wallace]]<br />
*[[Kay Mathiesen]]<br />
*[[Kim Dotcom]]<br />
*[[Kickstarter]]<br />
*[[Kind of Bloop]]<br />
<br />
=== L ===<br />
----<br />
*[[LambdaMOO]]<br />
*[[Larry Ellison]]<br />
*[[Lawrence Lessig]]<br />
*[[League of Legends]]<br />
*[[The League (Dating App)]]<br />
*[[LikeALittle]]<br />
*[[Limewire]]<br />
*[[Line (Application)]]<br />
*[[LinkedIn]]<br />
*[[Linus Torvalds]]<br />
*[[Live Video]]<br />
*[[Location targeted advertising]]<br />
*[[Lookbook.nu]]<br />
*[[Loot Box]]<br />
*[[Love Plus]]<br />
*[[Low Orbit Ion Cannon]]<br />
*[[Luciano Floridi]]<br />
*[[Lyft]]<br />
<br />
=== M ===<br />
----<br />
*[[Machine learning in healthcare]]<br />
*[[macOS]]<br />
*[[Manhunt]]<br />
*[[MapleStory]]<br />
*[[Mark Zuckerberg]]<br />
*[[Mashup]]<br />
*[[Mass Effect]]<br />
*''the'' [[The Matrix|Matrix]]<br />
*[[Internet meme|Meme]]<br />
*[[Mechanical Turk]]<br />
*[[Megaupload]]<br />
*[[Mia Consalvo]]<br />
*[[Microsoft chatbots]]<br />
*[[Microtransactions]]<br />
*[[Miguel Sicart]]<br />
*[[Military Entertainment Complex]]<br />
*[[Minecraft]]<br />
*[[Mirai Botnet]]<br />
*[[Misinformation]]<br />
*[[MMORPGs]]<br />
*[[Mods]]<br />
*[[MOOC (Massive Open Online Courses)]]<br />
*[[Moore's Law]]<br />
*[[Morris Worm]]<br />
*[[Mortal Kombat]]<br />
*[[Mr. Robot]]<br />
*[[Music piracy]]<br />
*[[Myspace]]<br />
<br />
=== N ===<br />
----<br />
*[[Napster]]<br />
*[[National Security Agency]]<br />
*[[NSA Cryptography]]<br />
*[[NCAA Football (Video Game Series)]]<br />
*[[Need For Speed (Video Game Series)]]<br />
*[[Nest Thermostat]]<br />
*[[Net neutrality]]<br />
*[[Netflix]]<br />
*[[Nextdoor]]<br />
*[[Norbert Wiener]]<br />
*[[Nosedive, Black Mirror]]<br />
*[[Nymwars]]<br />
<br />
=== O ===<br />
----<br />
*[[OK The Pirate Bay]]<br />
*[[Omegle]]<br />
*''Online'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] ''in Cyberspace''<br />
**[[Online Cheating|Cheating]]<br />
**[[Online Dating|Dating]]<br />
**[[Online Gambling|Gambling]]<br />
**[[Online Identity|Identity]]<br />
**[[Online Identity Theft|Identity Theft]]<br />
**[[Libel Online|Libel]]<br />
**[[Online Reputation Systems|Reputation Systems]]<br />
**''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Online shopping|Shopping]]<br />
**[[Cyberstalking|Stalking]] ''in CyberSpace''<br />
*[[Old School Runescape]]<br />
*[[The Open Internet|Open Internet]]<br />
*[[Onavo]]<br />
*[[OnStar]]<br />
*[[OpenAi]]<br />
*[[Orwell (Video Game)]]<br />
*[[Overwatch]]<br />
*[[Open Source Software]]<br />
<br />
=== P ===<br />
----<br />
*[[Palantir Technologies]]<br />
*[[Pandora]]<br />
*[[Parasocial Relationship]]<br />
*[[PARO Therapeutic Robot]]<br />
*[[Parody]]<br />
*[[Patents]]<br />
*[[PayPal]]<br />
*[[Periscope]]<br />
*[[Peter Thiel]]<br />
*[[Philip Brey]]<br />
*''Photo'' {{Relation|issues of|Digital Photography|#D}}<br />
**[[Photo Editing|Editing]]<br />
**[[Phototruth|Truth]]<br />
*[[Pinterest]]<br />
*[[The Pirate Bay|Pirate Bay]]<br />
*[[Plagiarism]]<br />
*[[PlayerUnknown's Battlegrounds]]<br />
*[[Pokémon Go]]<br />
*[[PokerStars]]<br />
*[[Pornography]]<br />
*[[Portal 2]]<br />
*[[Postal 2]]<br />
*[[Prank YouTubers]]<br />
*''Privacy''<br />
**[[Facebook Privacy Policy|Facebook Policy]]<br />
**''in'' [[Privacy in the China|China]]<br />
**''in'' [[Privacy in the Online Environment|Online Enviornment]]<br />
**''in'' [[Privacy in Social Networking|Social Networking]]<br />
**''in'' [[Privacy in public]]<br />
**[[Privacy Policies at Apple Inc.|Apple Policy]]<br />
*[[Privacy in Venmo]]<br />
*[[Privacy in the China]]<br />
*[[Pro-Ana Forums]]<br />
*[[Protect IP Act]]<br />
*[[Proxy Culture]]<br />
*[[Public Morality]]<br />
*[[The Punisher]]<br />
*[[Punishments in Virtual Environments]]<br />
<br />
===Q ===<br />
-----<br />
*[[Quora]]<br />
=== R ===<br />
----<br />
*[[Racial Algorithmic Bias]]<br />
*[[Racism in Video Games]]<br />
*[[Radio-frequency Identification]]<br />
*[[Ransomware]]<br />
*[[Raph Koster]]<br />
*[[Ray Kurzweil]]<br />
*[[Real Fake Page]]<br />
*[[Real Money Trade]]<br />
*[[Recommender Systems]]<br />
*[[Reddit]]<br />
**[[/r/AmITheAsshole]]<br />
**[[/r/wallstreetbets]]<br />
**[[/r/2meirl4meirl]]<br />
*[[Reid Hoffman]]<br />
*[[Renren]]<br />
*[[Richard Stallman]]<br />
*[[Right to be Forgotten]]<br />
*[[RIP Trolling]]<br />
*[[Rockmelt]]<br />
<br />
=== S ===<br />
------<br />
*[[Sampling (hip hop)]]<br />
*[[Self Driving Cars]]<br />
*[[Sergey Aleynikov]]<br />
*[[Serious Games]]<br />
*[[Sexting]]<br />
*[[Sharing Subscription Services]]<br />
*''Sims''<br />
**[[The Sims 3|The Sims 3]]<br />
**[[The Sims Online|The Sims Online]]<br />
**[[The Sims 4|The Sims 4]]<br />
*[[Slack (Application)]]<br />
*[[Smart Doorbell]]<br />
*[[Smart Home]]<br />
*[[Smartphones (Location Services)]]<br />
*[[Soccer & FIFA]]<br />
*[[Social Credit System]]<br />
*''Social''<br />
**[[Social Media in Sports|Media in Sports]]<br />
**[[Social media in national elections (2016)]]<br />
**[[Social Networking|Networking]]<br />
**[[Social Networking Services|Networking Services]] {{Relation|for sites|Facebook|Facebook}} {{Relation||Tianya.cn|Tianya.cn}} {{Relation||Twitter|Twitter}} {{Relation||Tumblr|Tumblr}}<br />
**[[Social Media (Meta)|Media (Meta)]]<br />
*[[Social media and the 2020 US presidential election]]<br />
*[[Social Media Websites in Investigations]]<br />
* [[Sousveillance]]<br />
*[[Snapchat]]<br />
*[[Spam]]<br />
*[[Spoof]]<br />
*[[Spotify]]<br />
*[[Spycams in South Korea]]<br />
*[[Starcraft II]]<br />
*[[Statistical Modeling]]<br />
*[[Steam]]<br />
*[[Steve Jobs]]<br />
*[[Stop Online Piracy Act]]<br />
*[[Student-Athlete Social Media Monitoring]]<br />
*[[StumbleUpon]]<br />
*[[Stuxnet Trojan]] {{Relation|type of|Worm|#W}} {{Relation|utilizes|Rootkit|#R}}<br />
*[[Surveillance Capitalism]]<br />
*[[Surveillance in China]]<br />
*[[Surveillance Technologies]]<br />
*[[Sword Art Online]]<br />
<br />
=== T ===<br />
------<br />
*[[Targeted Advertising (Online)]]<br />
*[[Team Fortress 2]]<br />
*[[Technological Determinism]]<br />
*[[Technological Singularity]]<br />
*[[Telepresence]]<br />
*[[Tencent]]<br />
*[[Tesla, Inc.]]<br />
*[[Testimonials]]<br />
*[[The Truman Show]]<br />
*[[Thomas M. Powers]]<br />
*[[Tianya.cn]]<br />
*[[TikTok]]<br />
*[[Tim Berners-Lee]]<br />
*[[Tinder]]<br />
*[[Tor]]<br />
*[[Transhumanism]]<br />
*[[Transparency in software development]]<br />
*[[Trustworthiness of information]]<br />
*[[Troll]]<br />
*[[Tumblr]]<br />
*[[Twitter]]<br />
*[[Twitch.tv]]<br />
<br />
=== U ===<br />
----<br />
*[[Uber]]<br />
*[[Ubiquitous Computing]]<br />
*[[Unabomber Manifesto]]<br />
*[[Uniqueness Debate]]<br />
*[[Undertale]]<br />
*[[Utilitarian Philosophy]]<br />
<br />
=== V ===<br />
----<br />
*[[Valve]]<br />
*[[Value Sensitive Design]]<br />
*[[Venmo]]<br />
*''Virtual'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Online|#Online}}<br />
**[[Virtual Assistants]]<br />
**''Bullying in'' [[Cyberbullying|Cyberspace]]<br />
**[[Virtual Child Pornography|Child Pornography]]<br />
**[[Virtual Community|Community]]<br />
**[[Virtual Crimes and Punishments|Crimes and Punishments]]<br />
** ''Dating ''[[Online Dating#Virtual_Dating|Online]]<br />
**[[Virtual Dating Simulations|Dating Simulations]]<br />
**[[Virtual Environment|Environment]]<br />
**[[Punishments in Virtual Environments|Punishment]]<br />
**[[Virtual Rape|Rape]]<br />
**''Sex in'' [[Cybersex|Cyberspace]]<br />
**''Stalking in'' [[Cyberstalking|Cyberspace]]<br />
*[[Virtual sweatshops]]<br />
*[[Violence and video games]]<br />
*[[Violence in Video Games]]<br />
*[[Virtual Magic Kingdom]]<br />
*[[Virtual Reality and Computer Simulations]]<br />
*[[Virtual Reality in Prison]]<br />
*[[Video Surveillance]]<br />
*[[Voice imitation algorithms]]<br />
*[[Vlogging]]<br />
*[[Vuze]]<br />
<br />
=== W ===<br />
----<br />
*[[Warcraft III]]<br />
*[[Watch Dogs]]<br />
*[[Watson (computer)]]<br />
*[[Wattpad]]<br />
*[[Waze]]<br />
*[[Wearable health tech]]<br />
*[[Web 2.0]]<br />
*[[Webcams]]<br />
*[[Webtoon App]]<br />
*[[WeChat]]<br />
*[[Weibo]]<br />
*[[Westworld and AI]]<br />
*[[WhatsApp]]<br />
*[[Whisper]]<br />
*[[Wii U]]<br />
*[[WikiLeaks]]<br />
*[[Wikipedia]]<br />
**[[Wikipedia Bots|Bots]]<br />
**[[Gender bias in Wikipedia]]<br />
*[[Witcher 3: Wild Hunt]]<br />
*[[Women in Gaming]]<br />
*[[World of Warcraft]]<br />
<br />
=== X ===<br />
----<br />
*[[The X-Files]]<br />
*[[Xkcd]]<br />
<br />
<br />
=== Y ===<br />
----<br />
*[[Yelp Reviewing]]<br />
*[[Yik Yak]]<br />
*''YouTube''<br />
**[[YouTube|YouTube (Website)]]<br />
**[[YouTube Beauty Community|Beauty Community]]<br />
**[[YouTube recommendation algorithm]]<br />
<br />
=== Z ===<br />
----<br />
*[[Zynga]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Topics&diff=91396Topics2021-01-13T14:39:11Z<p>WikiSysop: /* R */</p>
<hr />
<div>http://si410ethics11.projects.si.umich.edu/images/topics.png<br />
<br />
''Please add your newly created pages to this list in alphabetical order, and remember to surround with the appropriate MediaWiki syntax (i.e.:'' <nowiki>*[[your page]]</nowiki>'').''<br />
<br />
== ToolKit ==<br />
<br><br />
{|style="margin: 0em auto 0em auto"<br />
|{{shadowBox|boxHeight=100|boxWidth=740}}<br />
{|style="margin: 8px auto 0px auto" align="center" border="0px"<br />
| width="100px" | [[File:HelpingHand.jpg|80x80px|link=Topics/Boost|Boost]]<br />
| width="100px" | [[File:Working.jpg|80x80px|link=:Category:Action Needed|Action Needed]]<br />
| width="100px" | [[File:Seedling.jpg|80x80px|link=Topics/seed articles|Seed Articles]]<br />
| width="100px" | [[File:Schedule.png|80x80px|link=:Category:Out of Date|'''Out of Date Pages''']]<br />
| width="100px" | [[File:Dictionary.png|80x80px|link=:Category:Definitions|Definitions]]<br />
| width="100px" | [[File:Talk.png|80x80px|link=Topics/AboutWikiStandard|Wiki Terminology Standardization]]<br />
| width="100px" | [[File:Templates.png|80x80px|link=Topics/UserTemplates|Templates for Pages]]<br />
| width="100px" | [[File:Garbage.png|80x80px|link=:Category:IncinerateTrash|Marked Pages for Deletion]]<br />
|-<br />
| [[Topics/Boost|'''Wiki<br>Productivity Tools''']]<br />
| [[:Category:Action Needed|'''Action Needed''']]<br />
| [[Topics/seed articles|'''Seed Articles''']]<br />
| [[:Category:Out of Date|<br>'''Out of Date Pages''']]<br />
| [[:Category:Definitions|'''Definitions''']]<br />
| [[Topics/AboutWikiStandard|'''Standardizing Terminology''']]<br />
| [[Topics/UserTemplates|'''Infoboxes and Templates''']]<br />
| [[:Category:IncinerateTrash|'''Pages Marked for Deletion''']]<br />
|}{{endBox}}<br />
|}<br />
__NOTOC__<br />
<br><br />
<br />
<br><br />
== Blue Star Articles ==<br />
<br/><br />
*[[:Category:BlueStar2019|Blue Star Articles (2019)]]<br />
*[[:Category:BlueStar2018|Blue Star Articles (2018)]]<br />
*[[:Category:Blue Star|Blue Star Articles (2017)]]<br />
*[[:Category:GoldStar|Gold Star Articles (2010-2016)]]<br />
<br><br />
<br />
== List of New Articles in 2020 ==<br />
<br><br />
[[:Category:2020New|New Articles 2020]]<br />
*[[:Category:2020Concept|Concept]]<br />
*[[:Category:2020Person|Person]]<br />
*[[:Category:2020Object|Object]]<br />
<br><br />
<br />
== New Articles in 2019 ==<br />
<br><br />
[[:Category:2019New|New Articles 2019]]<br />
<br><br />
<br />
== John Walsh Thesis Revision ==<br />
<br/><br />
*[[John Walsh Thesis Revision]]<br />
<br />
== Portals and Class Writing Exercises ==<br />
<br><br />
*[[:Portal:Life on Digital Worlds|Life on Digital Worlds]]<br />
<br/><br />
<br />
== Categories ==<br />
<br><br />
{| style="width:400px;"<br />
! width="250"|Category<br />
! style="width:150px;text-align:center"|Number of Pages<br />
|-<br />
|[[:Category:Action Needed|Action Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Action Needed}}<br />
|-<br />
|[[:Category:Censorship|Censorship]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Censorship}}<br />
|-<br />
|[[:Category:Citations Needed|Citations Needed]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Citations Needed}}<br />
|-<br />
|[[:Category:Computer Simulation|Computer Simulation]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Computer Simulation}}<br />
|-<br />
|[[:Category:Concepts|Concepts]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Concepts}}<br />
|-<br />
|[[:Category:Corporations|Corporations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Corporations}}<br />
|-<br />
|[[:Category:Cyberpunk (genre)|Cyberpunk]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Cyberpunk (genre)}}<br />
|-<br />
|[[:Category:Hardware|Hardware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Hardware}}<br />
|-<br />
|[[:Category:Information Ethics|Information Ethics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Information Ethics}}<br />
|-<br />
|[[:Category:Internet slang|Internet Slang]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Internet slang}}<br />
|-<br />
|[[:Category:Malware|Malware]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Malware}}<br />
|-<br />
|[[:Category:Media Content|Media Content]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Media Content}}<br />
|-<br />
|[[:Category:Missing Information|Missing Information]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Missing Information}}<br />
|-<br />
|[[:Category:Music|Music]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Music}}<br />
|-<br />
|[[:Category:Open Source Projects|Open Source Projects]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Open Source Projects}}<br />
|-<br />
|[[:Category:Organizations|Organizations]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Organizations}}<br />
|-<br />
|[[:Category:Out of Date|Out of Date]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Out of Date}}<br />
|-<br />
|[[:Category:People|People]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:People}}<br />
|-<br />
|[[:Category:Piracy|Piracy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Piracy}}<br />
|-<br />
|[[:Category:Politics|Politics]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Politics}}<br />
|-<br />
|[[:Category:Portals|Portals]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Portals}}<br />
|-<br />
|[[:Category:Privacy|Privacy]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Privacy}}<br />
|-<br />
|[[:Category:Services|Services]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Services}}<br />
|-<br />
|[[:Category:Social Networking|Social Networking]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Social Networking}}<br />
|-<br />
|[[:Category:Software|Software]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Software}}<br />
|-<br />
|[[:Category:Sports|Sports]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Sports}}<br />
|-<br />
|[[:Category:Video Games|Video Games]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Video Games}}<br />
|-<br />
|[[:Category:Virtual Environments, Concerns, & Issues|Virtual Environments]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Virtual Environments, Concerns, & Issues}}<br />
|-<br />
|[[:Category:Websites|Websites]]<br />
|style="text-align:center"| {{PAGESINCATEGORY:Websites}}<br />
|}<br />
<br><br />
<br />
== Topics ==<br />
<br><br />
{{Section-Menu}}<br />
{{Section|||1}}<br />
=== # ===<br />
----<br />
*[[3D printing]]<br />
*[[4chan]]<br />
*[[4shared]]<br />
*[[8tracks]]<br />
*[[9GAG]]<br />
<br />
=== A ===<br />
----<br />
*[[Aaron Swartz]]<br />
*[[Adblocking]]<br />
*[[AdverGaming]]<br />
*[[Advertising ethics online]]<br />
*[[Aging In Place Technology]]<br />
*[[Airbnb]]<br />
*[[Alex Jones]]<br />
*[[Algorithmic Justice League]]<br />
*[[Algorithms]]<br />
*[[Amazon Alexa (Amazon Echo)]]<br />
*[[Amazon.com]]<br />
*[[Ancestry data]]<br />
*''the'' [[The Amy Boyer Case|Amy Boyer Case]]<br />
*[[Android]]<br />
*[[Angry Birds]]<br />
*''Anonymous''<br />
**[[Anonymous Behavior in Virtual Environments|Behavior in Virtual Environment]]<br />
**[[Anonymous (group)|Group]]<br />
*[[Apex Legends (game)]]<br />
*[[Applicant tracking systems]]<br />
*[[Artificial Agents]]<br />
*[[Artificial Intelligence and Technology]]<br />
*[[Artificial Intelligence in China]]<br />
*[[Artificial SuperIntelligence]]<br />
*[[Ashley Madison (website)]]<br />
*[[Assassin's Creed (Main Series)]]<br />
*[[Athletes and burner accounts]]<br />
*[[Augmented Reality]]<br />
*[[Automatic gender recognition]]<br />
*[[Autonomous Systems]]<br />
*[[Autonomous Vehicles]]<br />
*[[Avatar]]<br />
<br />
=== B ===<br />
----<br />
*[[Banality of Simulated Evil]]<br />
*[[Bandcamp]]<br />
*[[Bartle Test]]<br />
*[[Battlestar Galactica (2004 TV Series)]]<br />
*[[Behavioral biometrics]]<br />
*[[Bias in Information]]<br />
*[[Biem App]]<br />
*[[Binge Watching]]<br />
*[[Biobanking]]<br />
*[[BioShock]]<br />
*[[BioWare]]<br />
*[[Bitcoins]]<br />
*[[Bitmoji]]<br />
*[[BitTorrent]]<br />
*[[Black Mirror]]<br />
*[[Black Twitter]]<br />
*[[Blizzard Entertainment]]<br />
*[[Borderlands (video game series)]]<br />
*[[Brain-Machine Interface]]<br />
*[[Bumble]]<br />
*[[BuzzFeed]]<br />
<br />
=== C ===<br />
----<br />
*[[Call of Duty]]<br />
*[[Cambridge Analytica]]<br />
*[[Cancel Culture]]<br />
*[[Carrier IQ]]<br />
*[[CEIU Thesis]]<br />
*[[Censorship]]<br />
*[[Censorship in China]]<br />
*[[Chatroulette]]<br />
*[[Cheating]]<br />
*[[Cheating in eSports]]<br />
*[[Cheating Technologies]]<br />
*[[Circumventing Internet Censorship]]<br />
*[[Citizendium]]<br />
*[[Civilization]]<br />
*[[Clash of Clans]]<br />
*[[Clearview AI]]<br />
*[[Click fraud]]<br />
*[[Clickbait]]<br />
*''Cloud''<br />
**[[Cloud Computing|Computing]]<br />
**[[Cloud Security|Security]]<br />
*[[Clueful Chatting]]<br />
*[[Cookies]]<br />
*[[Complex]]<br />
*[[Confidentiality of Online Data]]<br />
*[[Content moderation]]<br />
*[[Content moderation in Twitter]]<br />
*[[Content moderation in Reddit]]<br />
*[[Counter-Strike: Global Offensive (video game)]]<br />
*[[Craigslist]]<br />
*[[Creative Commons]]<br />
*[[Criminal sentencing software]]<br />
*[[Crowdsourcing]]<br />
*[[Cryptocurrency]]<br />
*''Cyber (overlaps with Online)''{{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] {{Relation|cases of|David Thorne|#D}}<br />
**[[Cybercurrency|Currency]]<br />
**[[Cyberlaw|Law]]<br />
**[[Cybersex|Sex]]<br />
***''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Cybersecurity]]<br />
**[[Cyberstalking|Stalking]] {{Relation|use of crowdsourcing|Human Flesh Search|#H}} {{Relation||Tiayna.cn|#T}} {{Relation|cases of|Amy Boyer|#A}}<br />
**[[Cyberwarfare|Warfare]]<br />
*[[Cybersecurity in Banking]]<br />
<br />
=== D ===<br />
----<br />
*[[Daily Fantasy Sports]]<br />
*[[Dark Patterns]]<br />
*[[Dark Web]]<br />
*''Data (overlaps with Information)''<br />
**[[Data Aggregation Online|Aggregation Online]]<br />
**[[Data Mining|Mining]]<br />
*[[Deontology]] <br />
*[[Data brokers]]<br />
*[[Datafication of Children]]<br />
*[[Dating Apps]]<br />
*[[David Thorne]]<br />
*[[Da Vinci Surgical System]]<br />
*[[Deepfake]]<br />
*[[Defcon (video game)]]<br />
*[[Detroit: Become Human]]<br />
*[[Deus Ex (Series)]]<br />
**[[Deus Ex: Human Revolution]]<br />
*[[Device implant]]<br />
*[[Diablo (Franchise)]]<br />
**[[Diablo II]]<br />
**[[Diablo III]]<br />
*[[Diaspora]]<br />
*''Digital''<br />
**[[Digital Property|Property]]<br />
*[[Digital_divide]]<br />
**[[Digital DJing|DJing]]<br />
**[[Digital Piracy|Piracy]]<br />
**[[Digital Rights Management|Rights Management]]<br />
*[[Disclosive Ethics]]<br />
*[[DoorDash]]<br />
*[[DNA Testing]]<br />
*[[Domain Name System]]<br />
*[[Downloadable Content in Video Games]]<br />
*[[Dragonfly]]<br />
*[[Drones]]<br />
*[[Dropbox]]<br />
*[[Drupal]]<br />
*[[Duke F*** List]]<br />
<br />
=== E ===<br />
----<br />
*[[eBay]]<br />
*[[Edward Castronova]]<br />
*[[Edward H. Spence]]<br />
*[[Edward Snowden]]<br />
*[[Elder Scrolls]]<br />
*[[Electronic Arts]]<br />
*[[Electric Sheep]]<br />
*[[Electronic voting systems]]<br />
*''Electronic''<br />
**[[Electronic Health Records|Health Records]]<br />
**[[Electronic Sports|Sports]]<br />
*[[Elizabeth Holmes]]<br />
*[[Elon Musk]]<br />
*[[Empathy in Gaming]]<br />
*[[Emoji]]<br />
*[[Employers and Online Privacy]]<br />
*[[The Entire History of You]]<br />
*''Ethics''<br />
**''and'' [[Data Equity]]<br />
**''in'' [[Ethics in Computer & Video Games|Computer & Video Games]]<br />
**''in'' [[Ethics in Hacking|Hacking]]<br />
**''of'' [[Information Ethics|Information]]<br />
*[[Ethical game design]]<br />
*[[Etsy]]<br />
*[[Experience Project]]<br />
<br />
=== F ===<br />
----<br />
*''Facebook''<br />
**[[Advertising on Facebook]]<br />
**[[Facebook|Company]]<br />
**[[Facebook Messenger]]<br />
**[[Facebook newsfeed curation]]<br />
**[[Facebook Privacy Policy|Privacy Policy]]<br />
**[[Data Mining and Manipulation]]<br />
**[[Facebook in Africa]]<br />
*[[FaceTime]]<br />
*[[Face recognition]]<br />
*[[Face recognition in law enforcement]]<br />
*[[Fake News]]<br />
*[[Fan fiction]]<br />
*[[Find My Friends]]<br />
*[[File Sharing]]<br />
*[[Filter Bubble]]<br />
*[[Final Fantasy XIV]]<br />
*[[Fitness Game]]<br />
*[[First Person Shooters]]<br />
*[[Flaming]]<br />
*[[Free Basics]]<br />
*[[Freedom of Expression]]<br />
*[[Freemium model]]<br />
<br />
=== G ===<br />
----<br />
*[[Galaxy S3]]<br />
*[[Game Addiction]]<br />
*[[Gamergate]]<br />
*[[Gattaca]]<br />
*[[Gender bias in Wikipedia]]<br />
*[[Gender in Video Games]]<br />
*[[Genealogy platforms]]<br />
*[[General Data Protection Regulation]]<br />
*[[Genetically Modified Food]]<br />
*[[Gene Editing]]<br />
*[[Genomics]]<br />
*[[Genovese Syndrome]]<br />
*[[Geographic Information Systems]]<br />
*[[George Hotz]]<br />
*[[Ghost Writing Online]]<br />
*[[Girls Around Me]]<br />
*[[GLANSER]]<br />
*''Google''<br />
**[[Google|Company]]<br />
**[[Google Books|Books]]<br />
**[[Google Glass| Google Glass]]<br />
**[[Google Home]]<br />
**[[Google Clips]]<br />
**[[Google Street View|Street View]]<br />
*[[Goohah]]<br />
*[[Grand Theft Auto IV]]<br />
*[[Grand Theft Auto V]]<br />
*[[Griefing]]<br />
*[[Grindr]]<br />
<br />
=== H ===<br />
----<br />
*[[Hackers]]<br />
*[[Hacking the 2016 US Presidential Election]]<br />
*[[Health Informatics]]<br />
*[[Her (film) (2013)]]<br />
*[[Her Interactive]]<br />
*[[Herman Tavani]]<br />
*[[High Frequency Trading]]<br />
*[[Hulu]]<br />
*[[Human Flesh Search]] {{Relation|related to|Tianya.cn|#T}}<br />
*[[Human Microchipping]]<br />
*[["Human out of the Loop" Military Systems]]<br />
*[[Humans (British TV Series)]]<br />
<br />
=== I ===<br />
----<br />
*[[iCloud]]<br />
*[[id Software]]<br />
*[[Imgur]]<br />
*[[Infamous (series)]]<br />
*[[Influencer Marketing]]<br />
*[[Infoglut]]<br />
*[[Informatics]]<br />
*''Information'' {{Relation|overlaps with|Data|#Data}}<br />
**[[Information Ethics|Ethics]]<br />
**[[Information Freedom|Freedom]]<br />
**[[Freedom_of_Information_policies|Freedom of Information Policy]] <br />
**[[Information Overload|Overload]]<br />
**[[Information Reliability|Reliability]]<br />
**[[Information Security|Security]]<br />
**[[Information Transparency|Transparency]]<br />
**[[Information Vandalism|Vandalism]]<br />
*[[Informational Friction]]<br />
*[[Infosphere]]<br />
*[[Instagram]]<br />
*[[Instagram Ads]]<br />
*[[Intellectual Property]]<br />
*[[Internet of things]]<br />
*''Internet'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Online|#Online}} {{Relation||Virtual|#Virtual}}<br />
**[[Internet Archive|Archive]]<br />
**[[Internet Censorship in Hong Kong|Censorship in Hong Kong]]<br />
**[[Internet Censorship in South Korea|Censorship in South Korea]]<br />
**[[Internet Censorship in the United Kingdom|Censorship in the United Kingdom]]<br />
**[[Cybersecurity Law in Vietnam|Censorship in Vietnam]]<br />
**''in'' [[Circumventing Internet Censorship|Circumventing Censorship]]<br />
**[[Internet Control|Control]]<br />
**[[Internet meme|Meme]]<br />
*[[Iris Recognition]]<br />
<br />
=== J ===<br />
----<br />
*[[Jack Dorsey]]<br />
*[[Jailbreaking]]<br />
*[[James H. Moor]]<br />
*[[Jeremy Bentham]]<br />
*[[John Weckert]]<br />
*[[Julian Dibbell]]<br />
<br />
=== K ===<br />
----<br />
*[[Kathleen Wallace]]<br />
*[[Kay Mathiesen]]<br />
*[[Kim Dotcom]]<br />
*[[Kickstarter]]<br />
*[[Kind of Bloop]]<br />
<br />
=== L ===<br />
----<br />
*[[LambdaMOO]]<br />
*[[Larry Ellison]]<br />
*[[Lawrence Lessig]]<br />
*[[League of Legends]]<br />
*[[The League (Dating App)]]<br />
*[[LikeALittle]]<br />
*[[Limewire]]<br />
*[[Line (Application)]]<br />
*[[LinkedIn]]<br />
*[[Linus Torvalds]]<br />
*[[Live Video]]<br />
*[[Location targeted advertising]]<br />
*[[Lookbook.nu]]<br />
*[[Loot Box]]<br />
*[[Love Plus]]<br />
*[[Low Orbit Ion Cannon]]<br />
*[[Luciano Floridi]]<br />
*[[Lyft]]<br />
<br />
=== M ===<br />
----<br />
*[[Machine learning in healthcare]]<br />
*[[macOS]]<br />
*[[Manhunt]]<br />
*[[MapleStory]]<br />
*[[Mark Zuckerberg]]<br />
*[[Mashup]]<br />
*[[Mass Effect]]<br />
*''the'' [[The Matrix|Matrix]]<br />
*[[Internet meme|Meme]]<br />
*[[Mechanical Turk]]<br />
*[[Megaupload]]<br />
*[[Mia Consalvo]]<br />
*[[Microsoft chatbots]]<br />
*[[Microtransactions]]<br />
*[[Miguel Sicart]]<br />
*[[Military Entertainment Complex]]<br />
*[[Minecraft]]<br />
*[[Mirai Botnet]]<br />
*[[Misinformation]]<br />
*[[MMORPGs]]<br />
*[[Mods]]<br />
*[[MOOC (Massive Open Online Courses)]]<br />
*[[Moore's Law]]<br />
*[[Morris Worm]]<br />
*[[Mortal Kombat]]<br />
*[[Mr. Robot]]<br />
*[[Music piracy]]<br />
*[[Myspace]]<br />
<br />
=== N ===<br />
----<br />
*[[Napster]]<br />
*[[National Security Agency]]<br />
*[[NSA Cryptography]]<br />
*[[NCAA Football (Video Game Series)]]<br />
*[[Need For Speed (Video Game Series)]]<br />
*[[Nest Thermostat]]<br />
*[[Net neutrality]]<br />
*[[Netflix]]<br />
*[[Nextdoor]]<br />
*[[Norbert Wiener]]<br />
*[[Nosedive, Black Mirror]]<br />
*[[Nymwars]]<br />
<br />
=== O ===<br />
----<br />
*[[OK The Pirate Bay]]<br />
*[[Omegle]]<br />
*''Online'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Virtual|#Virtual}}<br />
**[[Cyberbullying|Bullying]] ''in Cyberspace''<br />
**[[Online Cheating|Cheating]]<br />
**[[Online Dating|Dating]]<br />
**[[Online Gambling|Gambling]]<br />
**[[Online Identity|Identity]]<br />
**[[Online Identity Theft|Identity Theft]]<br />
**[[Libel Online|Libel]]<br />
**[[Online Reputation Systems|Reputation Systems]]<br />
**''Sex in'' [[Online Dating#Sex|Online Dating]]<br />
**[[Online shopping|Shopping]]<br />
**[[Cyberstalking|Stalking]] ''in CyberSpace''<br />
*[[Old School Runescape]]<br />
*[[The Open Internet|Open Internet]]<br />
*[[Onavo]]<br />
*[[OnStar]]<br />
*[[OpenAi]]<br />
*[[Orwell (Video Game)]]<br />
*[[Overwatch]]<br />
*[[Open Source Software]]<br />
<br />
=== P ===<br />
----<br />
*[[Palantir Technologies]]<br />
*[[Pandora]]<br />
*[[Parasocial Relationship]]<br />
*[[PARO Therapeutic Robot]]<br />
*[[Parody]]<br />
*[[Patents]]<br />
*[[PayPal]]<br />
*[[Periscope]]<br />
*[[Peter Thiel]]<br />
*[[Philip Brey]]<br />
*''Photo'' {{Relation|issues of|Digital Photography|#D}}<br />
**[[Photo Editing|Editing]]<br />
**[[Phototruth|Truth]]<br />
*[[Pinterest]]<br />
*[[The Pirate Bay|Pirate Bay]]<br />
*[[Plagiarism]]<br />
*[[PlayerUnknown's Battlegrounds]]<br />
*[[Pokémon Go]]<br />
*[[PokerStars]]<br />
*[[Pornography]]<br />
*[[Portal 2]]<br />
*[[Postal 2]]<br />
*[[Prank YouTubers]]<br />
*''Privacy''<br />
**[[Facebook Privacy Policy|Facebook Policy]]<br />
**''in'' [[Privacy in the China|China]]<br />
**''in'' [[Privacy in the Online Environment|Online Enviornment]]<br />
**''in'' [[Privacy in Social Networking|Social Networking]]<br />
**''in'' [[Privacy in public]]<br />
**[[Privacy Policies at Apple Inc.|Apple Policy]]<br />
*[[Privacy in Venmo]]<br />
*[[Privacy in the China]]<br />
*[[Pro-Ana Forums]]<br />
*[[Protect IP Act]]<br />
*[[Proxy Culture]]<br />
*[[Public Morality]]<br />
*[[The Punisher]]<br />
*[[Punishments in Virtual Environments]]<br />
<br />
===Q ===<br />
-----<br />
*[[Quora]]<br />
=== R ===<br />
----<br />
*[[Racial Algorithmic Bias]]<br />
*[[Racism in Video Games]]<br />
*[[Radio-frequency Identification]]<br />
*[[Ransomware]]<br />
*[[Raph Koster]]<br />
*[[Ray Kurzweil]]<br />
*[[Real Fake Page]]<br />
*[[Real Money Trade]]<br />
*[[Recommender Systems]]<br />
*[[Reddit]]<br />
**[[/r/AmITheAsshole]]<br />
**[[/r/wallstreetbets]]<br />
**[[/r/2meirl4meirl]]<br />
*[[Reid Hoffman]]<br />
*[[Renren]]<br />
*[[Richard Stallman]]<br />
*[[Right to be Forgotten]]<br />
*[[RIP Trolling]]<br />
*[[Rockmelt]]<br />
<br />
=== S ===<br />
------<br />
*[[Sampling (hip hop)]]<br />
*[[Self Driving Cars]]<br />
*[[Sergey Aleynikov]]<br />
*[[Serious Games]]<br />
*[[Sexting]]<br />
*[[Sharing Subscription Services]]<br />
*''Sims''<br />
**[[The Sims 3|The Sims 3]]<br />
**[[The Sims Online|The Sims Online]]<br />
**[[The Sims 4|The Sims 4]]<br />
*[[Slack (Application)]]<br />
*[[Smart Doorbell]]<br />
*[[Smart Home]]<br />
*[[Smartphones (Location Services)]]<br />
*[[Soccer & FIFA]]<br />
*[[Social Credit System]]<br />
*''Social''<br />
**[[Social Media in Sports|Media in Sports]]<br />
**[[Social media in national elections (2016)]]<br />
**[[Social Networking|Networking]]<br />
**[[Social Networking Services|Networking Services]] {{Relation|for sites|Facebook|Facebook}} {{Relation||Tianya.cn|Tianya.cn}} {{Relation||Twitter|Twitter}} {{Relation||Tumblr|Tumblr}}<br />
**[[Social Media (Meta)|Media (Meta)]]<br />
*[[Social media and the 2020 US presidential election]]<br />
*[[Social Media Websites in Investigations]]<br />
* [[Sousveillance]]<br />
*[[Snapchat]]<br />
*[[Spam]]<br />
*[[Spoof]]<br />
*[[Spotify]]<br />
*[[Spycams in South Korea]]<br />
*[[Starcraft II]]<br />
*[[Statistical Modeling]]<br />
*[[Steam]]<br />
*[[Steve Jobs]]<br />
*[[Stop Online Piracy Act]]<br />
*[[Student-Athlete Social Media Monitoring]]<br />
*[[StumbleUpon]]<br />
*[[Stuxnet Trojan]] {{Relation|type of|Worm|#W}} {{Relation|utilizes|Rootkit|#R}}<br />
*[[Surveillance Capitalism]]<br />
*[[Surveillance in China]]<br />
*[[Surveillance Technologies]]<br />
*[[Sword Art Online]]<br />
<br />
=== T ===<br />
------<br />
*[[Targeted Advertising (Online)]]<br />
*[[Team Fortress 2]]<br />
*[[Technological Determinism]]<br />
*[[Technological Singularity]]<br />
*[[Telepresence]]<br />
*[[Tencent]]<br />
*[[Tesla, Inc.]]<br />
*[[Testimonials]]<br />
*[[The Truman Show]]<br />
*[[Thomas M. Powers]]<br />
*[[Tianya.cn]]<br />
*[[TikTok]]<br />
*[[Tim Berners-Lee]]<br />
*[[Tinder]]<br />
*[[Tor]]<br />
*[[Transhumanism]]<br />
*[[Transparency in software development]]<br />
*[[Trustworthiness of information]]<br />
*[[Troll]]<br />
*[[Tumblr]]<br />
*[[Twitter]]<br />
*[[Twitch.tv]]<br />
<br />
=== U ===<br />
----<br />
*[[Uber]]<br />
*[[Ubiquitous Computing]]<br />
*[[Unabomber Manifesto]]<br />
*[[Uniqueness Debate]]<br />
*[[Undertale]]<br />
*[[Utilitarian Philosophy]]<br />
<br />
=== V ===<br />
----<br />
*[[Valve]]<br />
*[[Value Sensitive Design]]<br />
*[[Venmo]]<br />
*''Virtual'' {{Relation|overlaps with|Cyber|#Cyber}} {{Relation||Internet|#Internet}} {{Relation||Online|#Online}}<br />
**[[Virtual Assistants]]<br />
**''Bullying in'' [[Cyberbullying|Cyberspace]]<br />
**[[Virtual Child Pornography|Child Pornography]]<br />
**[[Virtual Community|Community]]<br />
**[[Virtual Crimes and Punishments|Crimes and Punishments]]<br />
** ''Dating ''[[Online Dating#Virtual_Dating|Online]]<br />
**[[Virtual Dating Simulations|Dating Simulations]]<br />
**[[Virtual Environment|Environment]]<br />
**[[Punishments in Virtual Environments|Punishment]]<br />
**[[Virtual Rape|Rape]]<br />
**''Sex in'' [[Cybersex|Cyberspace]]<br />
**''Stalking in'' [[Cyberstalking|Cyberspace]]<br />
*[[Virtual sweatshops]]<br />
*[[Violence and video games]]<br />
*[[Violence in Video Games]]<br />
*[[Virtual Magic Kingdom]]<br />
*[[Virtual Reality and Computer Simulations]]<br />
*[[Virtual Reality in Prison]]<br />
*[[Video Surveillance]]<br />
*[[Voice imitation algorithms]]<br />
*[[Vlogging]]<br />
*[[Vuze]]<br />
<br />
=== W ===<br />
----<br />
*[[Warcraft III]]<br />
*[[Watch Dogs]]<br />
*[[Watson (computer)]]<br />
*[[Wattpad]]<br />
*[[Waze]]<br />
*[[Wearable health tech]]<br />
*[[Web 2.0]]<br />
*[[Webcams]]<br />
*[[Webtoon App]]<br />
*[[WeChat]]<br />
*[[Weibo]]<br />
*[[Westworld and AI]]<br />
*[[WhatsApp]]<br />
*[[Whisper]]<br />
*[[Wii U]]<br />
*[[WikiLeaks]]<br />
*[[Wikipedia]]<br />
**[[Wikipedia Bots|Bots]]<br />
**[[Gender bias in Wikipedia]]<br />
*[[Witcher 3: Wild Hunt]]<br />
*[[Women in Gaming]]<br />
*[[World of Warcraft]]<br />
<br />
=== X ===<br />
----<br />
*[[The X-Files]]<br />
*[[Xkcd]]<br />
<br />
<br />
=== Y ===<br />
----<br />
*[[Yelp Reviewing]]<br />
*[[Yik Yak]]<br />
*''YouTube''<br />
**[[YouTube|YouTube (Website)]]<br />
**[[YouTube Beauty Community|Beauty Community]]<br />
**[[YouTube recommendation algorithm]]<br />
<br />
=== Z ===<br />
----<br />
*[[Zynga]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91392User:WikiSysop2021-01-13T13:15:23Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
I am crazy for [https://frank-turner.com/home/ Frank Turner], a British punk-folk singer-songwriter extraordinaire. I have seen him live 18 times since 2015. During 2020 lockdown, his best-streamed shows were #2500 from Oxford UK and the awesome #2012 from Kirkby UK on 28 December 2020, which just so happened to be his 39th birthday.</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=User:WikiSysop&diff=91391User:WikiSysop2021-01-13T13:06:56Z<p>WikiSysop: </p>
<hr />
<div>Paul Conway is the WikiSysop for the SI 410 MediaWiki site. WikiSysop has superpowers, including the ability to delete pages, block users, and control spam.<br />
<br />
[[File:ConwayShades.jpg|200px|thumb|left|Paul Conway]]Paul Conway is associate professor emeritus at the University of Michigan School of Information. I conduct research and teach courses on the digitization and preservation of photographs, books, and audiovisual resources, archival science, and the ethics of new technologies. <br />
----<br />
Prior to joining the Michigan faculty in 2006, I was a senior administrator for the libraries at Yale University and Duke University. I have a Ph.D. from the University of Michigan. I attended Indiana University as an undergraduate. <br />
=About the arm=<br />
The arm in the picture is that of my youngest son Mike. He's almost 24. I also have an older son Patrick, a graduate of MSU. Mike and Patrick live in Charlotte in Charlotte, North Carolina. My daughter Nina and her husband Russell also live in Charlotte with their daughter Penelope Ann. She's too cute and almost 7. My wife Martha is a librarian and directs the Special Collections Research Center at the University of Michigan. We met over 30 years ago at Dominic's -- at happy hour.<br />
<br />
==Me and my GPS==<br />
[[File:Mavis_concert_poster.jpg|right|thumb|200px|I'll take you there!]]<br />
<br />
For a decade, I used a standalone Garmin GPS device in my car. I stuck it to the windshield. My GPS is particularly helpful while driving in Boston. That city has the craziest road patterns this side of Istanbul.<br />
<br />
Some people name their devices. I named my GPS Mavis for Mavis Staples. Even more explicitly, my GPS channels the Staples Singers' song. "I'll Take You There." Get it?<br />
<br />
Mavis the singer is taking us to heaven. Mavis the GPS is merely helping me survive Boston. Good enough.<br />
<br />
<br />
=My Frank Turner Obsession=<br />
<br />
Everyone in our family is crazy about Frank Turner, a British punk-folk singer-songwriter extraordinaire. More to come.</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Hacking_the_2016_US_Presidential_Election&diff=91372Hacking the 2016 US Presidential Election2020-04-03T16:03:07Z<p>WikiSysop: </p>
<hr />
<div>{{Nav-Bar|Topics##}}<br><br />
<br />
[[File:Johnpodesta.jpg|thumbnail|Hillary Clinton's campaign chairman John Podesta, whose email was hacked by GRU-supported groups.]]<br />
<br />
[[File:Phishing.jpg|thumbnail|Leaked Diagram from NSA document about Russian Election Interference ]]<br />
In early 2016, the Russian Federation began a coordinated hacking effort against the Democratic National Committee and Presidential candidate Hillary Rodham Clinton. <ref> “Russian Interference in the 2016 United States Elections.” Wikipedia, Wikimedia Foundation, 3 Mar. 2020, en.wikipedia.org/wiki/Russian_interference_in_the_2016_United_States_elections.</ref> This was a part of a larger effort to influence the 2016 Presidential election. Hackers used a diverse range of methods to gain access to confidential emails. In addition, they targeted election systems in numerous states. Close to 150,000 emails were stolen by Russian government-backed hacking groups and were posted online <ref> Balsamo, Michael. “Thousands of Emails Stolen from Republican Committee's Aides.” AP NEWS, Associated Press, 5 Dec. 2018, apnews.com/cdcda481c0574b4da214703000e350b8. </ref>. It is not believed that the efforts to tamper with voting systems were effective, but intelligence officials still have doubts. <br />
<br />
== Hacking The DNC and Hillary Clinton ==<br />
The first hacks began in March 2016 <ref> Chang, Alvin. “How Russian Hackers Stole Information from Democrats, in 3 Simple Diagrams.” Vox, Vox, 16 July 2018, www.vox.com/policy-and-politics/2018/7/16/17575940/russian-election-hack-democrats-trump-putin-diagram. </ref>. Clinton staffers and volunteers began to be targeted through email phishing. Over the course of five days, dozens of target emails were sent. Phishing is when hackers send out malicious emails that trick users into giving sensitive information. One common method is asking users to reset their password for their email through a phony form that sends credentials to a hacker. Through this process, the hackers were able to gain access to the email of John Podesta, who was Clinton’s campaign chairman at the time. Using credentials gained from phishing, the hackers were also able to log in as System Administrators on the Democratic Congressional Campaign Committee (DCCC) five days later and then, using information from there, the Democratic National Committee (DNC) network. Once on the networks, the hackers used Minikatz to collect login credentials, X-Agent to take screenshots and log keystrokes, and X-Tunnel to send information back to hacker-controlled servers. The data first went to middle servers hosted in Arizona. In the Mueller Report, it was stated that these servers worked as a buffer to avoid detection. <ref> Whittaker, Zack. “Mueller Report Sheds New Light on How the Russians Hacked the DNC and the Clinton Campaign.” TechCrunch, TechCrunch, 18 Apr. 2019, techcrunch.com/2019/04/18/mueller-clinton-arizona-hack/. </ref> All the tools used were open sourced and readily available to the public. The stolen information was distributed first in June 2016 through a website that was created, DCLeaks.com. These leaks were claimed by a hacker going by the moniker “Guccifer 2.0”, who professed to be a lone Romanian. Eventually, Guccifer 2.0 got in contact with WikiLeaks. WikiLeaks asked for new material that could be released at the Democratic National Convention in order to sow discord. Thus, in late July, WikiLeaks started releasing documents, which included 20,000 of the Podesta emails. After 2016, it was found that two divisions directly connected to the Russian Military Intelligence Service (GRU) had been directly connected to the email hacks. The group known as Fancy Bear comprised multiple units with distinct tasks to interfere with the U.S. elections. In the Mueller report, Unit 26165 was blamed for overseeing the hacking of the DNC, while Unit 74455 oversaw the dissemination and publication of documents stolen in the hack. It was also found that Brittany Kaiser at [http://si410wiki.sites.uofmhosting.net/index.php/Cambridge_Analytica Cambridge Analytica ] met Julian Assange of [http://si410wiki.sites.uofmhosting.net/index.php/WikiLeaks Wiki Leaks] in February 2017. <ref> Cadwalladr, Carole, and Stephanie Kirchgaessner. “Cambridge Analytica Director 'Met Assange to Discuss U.S. Election'.” The Guardian, Guardian News and Media, 7 June 2018, www.theguardian.com/uk-news/2018/jun/06/cambridge-analytica-brittany-kaiser-julian-assange-wikileaks. </ref> Reporters also discovered that, in 2016, Cambridge Analytica reached out to Assange asking to index and distribute the emails that had been stolen. In October 2018, multiple GRU officers were charged by the U.S. Department of Justice with hacking and disinformation. <ref> “U.S. Charges Russian GRU Officers with International Hacking and Related Influence and Disinformation Operations.” The United States Department of Justice, 4 Oct. 2018, www.justice.gov/opa/pr/us-charges-russian-gru-officers-international-hacking-and-related-influence-and. </ref><br />
<br />
== Hacking of Voting Systems ==<br />
In July 2019, the Senate Intelligence Committee concluded that, in 2016, election systems in all 50 states were targeted by hackers tied to the Russians. <ref> Sanger, David E., and Catie Edmondson. “Russia Targeted Election Systems in All 50 States, Report Finds.” The New York Times, 25 July 2019, www.nytimes.com/2019/07/25/us/politics/russian-hacking-elections.html. </ref> The report concluded that there was no evidence of votes being changed. There was a strong belief, however, that Russian intelligence may have found a way into voting systems and had possibly decided not to act. It was revealed that close to ten phishing emails were sent to local election officials days before the 2016 election, and at least one U.S. voting software company was targeted. Local election officials were sent emails pretending to be from voting software companies to open Microsoft Word documents with malware. Through this, hackers possibly gained access to the voting systems. A leaked NSA report indicated that Russian hackers may have penetrated voting systems further than previously understood. <ref> Esposito, Richard, et al. “Top-Secret NSA Report Details Russian Hacking Effort Days Before 2016 Election.” The Intercept, 5 June 2017, theintercept.com/2017/06/05/top-secret-nsa-report-details-russian-hacking-effort-days-before-2016-election/.</ref> It is unknown, however, the extent and effect of these hacking efforts. <br />
<br />
== Ethics ==<br />
The main ethical concerns in the 2016 election related to privacy and election integrity. Emails were illegally stolen from a private server that revealed campaign strategy and the Clinton campaign’s donor relationships. Additionally, efforts to undermine voting in the election have revealed flaws in current voting software and the private companies that make this software --flaws that experts believe could be exploited in the future.<br />
=== Privacy ===<br />
The release of the Podesta emails revealed Hillary Clinton’s distinct relationship with Clinton Foundation donors, with Wall Street, and with her staffers.<ref> Stein, Jeff. “What 20,000 Pages of Hacked WikiLeaks Emails Teach Us about Hillary Clinton.” Vox, 20 Oct. 2016, www.vox.com/policy-and-politics/2016/10/20/13308108/wikileaks-podesta-hillary-clinton. </ref> One email that was especially damning came from research director Tony Carrk. It was an email of a Clinton speech titled with the header Clinton Admits She Is Out of Touch. <ref> Cheney, Kyle, et al. “The Most Revealing Clinton Campaign Emails in WikiLeaks Release.” POLITICO, 8 Oct. 2016, www.politico.com/story/2016/10/john-podesta-wikileaks-hacked-emails-229304.<br />
</ref> In the speech to Wall Street donors, Clinton talked about how she had lost her connection to Middle Class America. The Republican National Committee used these emails to undermine Clinton’s campaign. RNC Chairman Reince Preibus stated,“The truth that has been exposed here is that the persona Hillary Clinton has adopted for her campaign is a complete and utter fraud.”<br />
<br />
=== Integrity and Authenticity ===<br />
In 2019, University of Michigan professor Alex Halderman made an important recommendation for 370 million dollars in federal funding to replace outdated voting machines. <ref> Crang, Steve. “Election Security: Halderman Recommends Actions to Ensure Integrity of U.S. Systems.” Michigan Engineering, 27 Feb. 2019, news.engin.umich.edu/2019/02/election-security-halderman-recommends-actions-to-ensure-integrity-of-us-systems/. </ref> This was after Halderman in 2018 showed how he was able to hack voting machines used in 10 states, to change the outcome of a fake University of Michigan vs. Ohio State University Election. <ref>Halderman, J. Alex. “I Hacked an Election. So Can the Russians.” The New York Times, The New York Times, 5 Apr. 2018, www.nytimes.com/2018/04/05/opinion/election-voting-machine-hacking-russians.html. </ref> Much of the concern involves how this hacking undermines the democratic process. In the aftermath of the 2016 election, many cybersecurity companies have offered their services for free to help shore up election security. <ref> Yakowicz, Will. “The White Hats in the War Against Election Meddling.” Inc.com, Inc., 23 July 2018, www.inc.com/will-yakowicz/cyber-companies-offer-state-election-websites-free-security-software.html. </ref> <br />
== References ==<br />
<br />
[[Category:2020New]]<br />
[[Category:2020Concept]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Postal_2&diff=91371Postal 22020-04-03T15:28:41Z<p>WikiSysop: </p>
<hr />
<div><br />
[[File:Postal_2_cover.png |thumb|300x300px|By Source, Fair use, https://en.wikipedia.org/w/index.php?curid=29824640]]Postal 2 is a first person shooter video game that was created by the company Running With Scissors, released originally in April 2003 for systems running Windows. The game is now available on Windows, Mac, and Linux systems. It is the second installment in the Postal quartet series. Postal 2 was faced with mass amounts of controversy due to the violent actions the player can decide to take part in. The game also received mass controversy for violence against animals, homophobia and racial/ethnic stereotypes, as well as drug and alcohol content. The game also features high amounts of inappropriate language and swearing. Despite being highly controversial and receiving poor reviews from players and critics alike, the game has managed to cultivate a cult following. The game has had five expansion packs available, with the latest one being released in 2015.<br />
<br />
==Story-line==<br />
The story of the game features the main character, “Postal Dude” running errands in the town of Paradise, Arizona. He lives in a trailer with his wife. The game follows Postal Dude as he sets out to perform a set of errands each day of the week, from Monday to Friday. Postal Dude is interrupted often by non player characters attacking him. Technically, the game is able to be finished without ever using violence, however it is difficult and the player character is repeatedly put into situations that one would assume would demand violence. Throughout the game, the player character encounters a variety of wacky and offensive characters that challenge their ability to accomplish each of their errands. The game has the player do tasks such as picking up their paycheck from work, cashing a check at the bank, and getting milk, as well as many others, which turn violent.<ref>Postal 2 Wiki https://postal.fandom.com/wiki/POSTAL_2</ref> When the Postal Dude goes to pick up his paycheck, he is fired and protesters converge on the property to attack the workers. When he goes to the bank, robbers attack. Other tasks that the player must perform include peeing on the Postal Dude’s father’s grave, encountering homicidal anti-book protesters at the library when returning a library book, being attacked by Al Qaeda in a church, and curing a case of Gonorrhea, as well as other tasks. The game ends on Friday with the apocalypse taking place, and the player must survive as they make their way back to the trailer.<br />
<br />
==Expansion Packs==<br />
[[File:Paradiselostimage.jpg |thumb|left|300x300px|Paradise Lost cover, source: https://www.humblebundle.com/store/agecheck/postal-2-paradise-lost]]Postal 2 has had five expansion packs. Running with Scissors released an updated version of the game called Postal 2: Share the Pain, which featured a new multiplayer mode in the game. Since 2008 it has been included in the base game. The second expansion pack, titled "Apocalypse Weekend," was released in 2004. It includes a sequel to the events of the original game. The player controls the Postal Dude after the events of Postal 2 as they navigate through the post apocalyptic world to recover his dog and his trailer. The expansion follows the Postal Dude through the weekend following the original game's events, as he fights zombies, terrorists, and military personnel. A third expansion, titled "Corkscrew Rules!" was released in 2005 by Avalon Style Entertainment, features a man named Corkscrew who discovers that his penis was amputated. The player is sent on a quest to recover Corkscrew's missing genitalia. The game was originally only released in Russia and Japan, however an English version was released for free via Steam in 2017. Postal 2: Eternal Damnation is the fourth expansion pack created for Postal 2, developed by Resurrection-Studios and released in 2005. The pack is a total conversion of Postal 2, changing the plot to feature a man named John Murray who is in a mental asylum. The final expansion pack released for Postal 2, Paradise Lost, was released in 2015. The story of Paradise Lost takes place 12 years after the events of Postal 2, in which the Postal Dude navigates the post apocalyptic world left from the events of the original game in search of his dog, Champ. <br />
<br />
==Ethical Issues==<br />
===Violence===<br />
Postal 2 is an extremely violent game and faced mass criticism and controversy. Postal 2 features several weapon types, including melee weapons, handguns, shotguns, machine guns, fuel-based, projectiles, toxic weapons, snipers, launchers, and napalm as well as other various weapon types. The player is able to murder any non player character that they find on the street, including police officers, using any of the weapons that are provided. The murders are graphic, with blood being shown. and in the cases of explosions, body parts being blown off. This has led to Postal 2 being banned in New Zealand, with the reasoning being the high amounts of violence as well as violence against animals. The game developers defend the game against its claims of high violence, stating that the game is able to be played without ever performing a violent act, however the player is placed in situations where violence is encouraged and is the easiest solution to the problem. The player is also allowed to commit suicide by putting a grenade in their mouth after pulling the pin, causing them to explode. When prompted whether the player would like to commit suicide or not, they are given the options "Press to end it all" or "press to wuss out," causing the choice not to commit suicide to appear as a cowardly decision.<br />
<br />
===Animal Abuse===<br />
The Postal Dude has the ability to urinate on animals and kill animals such as dogs, cats, elephants, cows, monkeys, and dinosaurs. The player can use cats as a silencer for their guns by sticking the barrel of their gun into the cat’s anus. The player has the option to urinate on anything, including animals. The player has the option to set the elephant found in the game on fire. The player is highly encouraged to kill animals during a part of the game where they encounter hostile junkyard dogs. Animal violence has been cited as one of the reasons for the game being banned in New Zealand. <ref>https://www.geek.com/games/11-video-games-that-got-banned-and-why-1645406/</ref><br />
<br />
===Homophobia and Racial and Ethnic Stereotypes===<br />
Postal 2 has several instances of homophobia built into the game. The player can use an arcade machine that has a known homophobic slur built into the name; homosexual non player characters are put into the game as stereotypes, and are available for the player to kill. Henk Kroll, who is an editor of a gay newspaper in the Netherlands, proposed a ban on Postal 2, arguing for the game being problematic for presenting homosexual people as a stereotype that the player can kill. The player also has the ability to open fire on gay pride and minority parades.<ref>https://web.stanford.edu/group/htgg/cgi-bin/drupal/sites/default/files2/hmayer_2003_1.pdf</ref> There are examples of racial and ethnic stereotyping in the game as well. Middle eastern men can be seen perusing the library, looking at books about terrorism, before they open fire on the player.<ref>http://www.cnn.com/2003/TECH/fun.games/06/05/postal2.game/</ref> The player is also attacked by Al Qaeda while at the church. There are Asian stereotypes presented in a Chinese restaurant the player must go to as well. <br />
<br />
===Drugs and Alcohol Content===<br />
[[File:Healthpipe.png |thumb|left|100x100px|Image of the Health Pipe item, source: https://postal.fandom.com/wiki/Health_Pipe]]The player is able to consume an item called the "Health Pipe," which causes the player to regain health temporarily and gain extra strength. When the affects of the Health Pipe are over, however, the player loses health and has a mini heart attack unless they consume another Health Pipe. According to the Postal 2 wiki, the Health Pipe is actually a crack cocaine pipe. <ref>https://postal.fandom.com/wiki/Health_Pipe</ref> Vodka is also featured in the game as a replacement for the Health Pipe. Upon consuming vodka, the player regains all of their health. In the original game, consuming vodka had no negative consequences. Only in the newer, Steam version of the game were consequences added for consuming vodka, including impaired aiming and withdrawal affects that cause health damage. <ref>https://postal.fandom.com/wiki/Vodka</ref><br />
<br />
==References==</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Privacy_in_the_China&diff=91345Privacy in the China2020-03-30T18:07:42Z<p>WikiSysop: </p>
<hr />
<div>In China, privacy means the information that is unrelated to any public benefit or any group interest; it’s the concealed personal information that an individual or a group feels unwilling or inconvenient to share with others. Privacy protection is a natural human right of each individual.<br />
<ref>Privacy in China |(汉语词语)_百度百科, https://baike.baidu.com/item/%E9%9A%90%E7%A7%81/12883.</ref><br />
<br />
In March 2001, the Chinese Supreme People's Court has announced the privacy protection law in the judicial interpretation. However, this privacy protection law didn't explain the conceptional difference between privacy and privacy law. It only emphasized "disobeying the public common interest, public morality, or invading others' privacy" but didn't explain whether the victim share receives protection when there's a conflict between the victim's privacy and the public common interest.<br />
<ref>Baidu.com. (2017), 个人隐私_百度百科, https://baike.baidu.com/item/%E4%B8%AA%E4%BA%BA%E9%9A%90%E7%A7%81.</ref><br />
__TOC__<br />
<br />
==Challenges in privacy protection==<br />
The development of informational technologies and the internet has brought new concerns about privacy protection. According to a media survey in China, in recent years, 55.8% of participants considered the protection their privacy had become harder than previous, 29.3% of them think their private information is revealed to the public "casually". <ref>Privacy |(汉语词语)_百度百科, https://baike.baidu.com/item/%E9%9A%90%E7%A7%81/12883.</ref><br />
<br />
As technology advanced, many means of privacy protection has emerged. For example, emails can now be encrypted via PGP and be anonymously sent through networks like I2P. Tor can also be used to prevent internet service providers from knowing who their clients are communicating with. Hypertext Transfer Protocol Secure (HTTPS) is an extension of the Hypertext Transfer Protocol (HTTP). <ref>HTTPS, Wikipedia Contributors (2020), [online] Wikipedia, https://en.wikipedia.org/wiki/HTTPS.</ref><br />
<br />
It's used for more secure encrypted communication over a computer network, and it's widely used on the internet since recent years. Currently, most websites in China have not adopted HTTPS. <ref>Let’s Talk About HTTPS, Google and China - The SSL StoreTM, [online] Hashed Out by The SSL StoreTM, https://www.thesslstore.com/blog/https-google-china/.</ref><br />
<br />
==The great firewall==<br />
When the internet first came to China in 1994, it was not strictly regulated. This was also the result of China's "Open Door Policy", which hoped to introduce more Western knowledge to reform the Chinese economy. However, as the popularity of internets grew, the former leader of China, Deng Xiaoping had concerns about the security of the country. In 2000, the Golden Shield Project was implemented, which is the precursor to the "Great Firewall". In 2018, the Chinese government employs over 50,000 people to enforce its censorship, including blocking websites, searching for disapproved information, and blocking personal accounts. <ref>Let’s Talk About HTTPS, Google and China - The SSL StoreTM, [online] Hashed Out by The SSL StoreTM, https://www.thesslstore.com/blog/https-google-china/ .</ref><br />
<br />
<br />
In recent years, the government institutions in China have started to apply more advanced informational technologies, such as machine learning and artificial intelligence, to more efficiently carry out its censorship and better regulate social behaviors.<br />
<br />
In July 2017, the Chinese state council released the Next Generation Artificial Intelligence Development Plan (NGAIDP). The plan covered strategies to become the leading AI power in both research and deployment by 2030. It advocates the incorporation of AI in all aspects of people's daily life, such as medicine, transportation, environmental protection, and education.<ref> Sixth Tone (2019), Camera Above the Classroom, [online] Medium, https://medium.com/@SixthTone/camera-above-the-classroom-532738e23d09. </ref><br />
<br />
<br />
<br />
==Traffic camera==<br />
[[File:traffic.jpg|Camera system|300px|thumb|right]]<br />
<br />
Computer vision is the technology that deals with how computers can understand images and videos from high-level. Furthermore, it means computers can substitute human to find and track the matched target in images or videos. <ref>Wikipedia Contributors (2020), Computer vision, [online] Wikipedia, https://en.wikipedia.org/wiki/Computer_vision .</ref><br />
[[File:traffic_facial.jpg|A driver who was using his cellphone while driving, captured by the traffic camera|300px|thumb|right]]<br />
<br />
In China, computer vision has been applied in the traffic camera system, to ease the pressure of traffic regulation. This new type of camera system, also known as the surveillance system. can not only recognize people on the street (mainly used for capturing criminals) but also tell whether the driver is using a cellphone or not wearing a seatbelt, etc. According to Qiu Rui, a policeman in Chongqing, was on duty in the summer of 2019. He received an alert from the surveillance system that there was a high probability caught on camera was a suspect in a 2002 murder case. <ref>Xinhuanet.com (2019), Chinese police nab murder suspect with facial recognition - Xinhua | English.news.cn, http://www.xinhuanet.com/english/2019-08/09/c_138296390.htm.</ref><br />
<br />
Undeniably, this new type of camera system is helping the police better maintaining the social order, better regulating the traffic, and fewer people break the traffic rules. However, the widespread cameras system also raises concerns about the violation of people's privacy. According to the International Covenant on Civil and Political Rights, both collection and use of biometric data should be limited to people found to be involved in wrongdoing, and not broad populations who have no specific link to crime. Individuals should have the right to know what biometric data the government holds on them, but the Chinese automated surveillance camera system has violated those standards.<ref>Keegan, M. (2019). Big Brother is watching: Chinese city with 2.6m cameras is world’s most heavily surveilled, [online] the Guardian, https://www.theguardian.com/cities/2019/dec/02/big-brother-is-watching-chinese-city-with-26m-cameras-is-worlds-most-heavily-surveilled.</ref><br />
<br />
==Surveillance system in school==<br />
In 2018, high schools in China have started to apply computer vision technology in their camera system, for security reasons and to better regulate the students' behavior. Those cameras are deployed in each corridor and each classroom, to find out students' absence and to supervise the students' performance in each class. According to Guo Yuzhuo, a high school biology teacher in Beijing, the system is able to scan through the facial expressions of all students in the classroom and tell whether they are paying attention or doing any other unrelated stuff. After the class, the system generates a report for each individual in the classroom, so she knows how the students spend their time in the 45 minutes. <ref> Sixth Tone (2019), Camera Above the Classroom, [online] Medium, https://medium.com/@SixthTone/camera-above-the-classroom-532738e23d09. </ref><br />
<br />
<br />
<br><br><br />
<br />
==Internet censorship==<br />
[[File:wechat.jpg|Wechat account blocked after posting sensitive contents|300px|thumb|right]]<br />
In China, the constitution law confirms that each individual has the freedom of speech, but meanwhile, it states, "while the people exercise this freedom, they can't cause any public chaos, they can't break any laws or cause damages to the society and the country". <br />
<br />
In order to better regulate social order on the internet, Machine Learning and Optical Character Recognition(OCR) technologies are widely used, to surveil people's behaviors online. In January 2010, Google announced that they were no longer willing to censor searches in China, and pull out of the country completely. At the same time, Google started to redirect all search queries from Google.cn to Google.com.hk in Hong Kong, which returned results without censorship. On September 9th, 2013, the Chinese Supreme People's Court has announced a new law that, if the online information is used to slander others, and it's clicked or viewed for more than 5,000 times, or reposted for more than 500 times, it shall be considered a defamation crime.<br />
<ref>Baidu.com. (2013). 转发500次_百度百科, https://baike.baidu.com/item/%E8%BD%AC%E5%8F%91500%E6%AC%A1.</ref><br />
<br />
==See Also==<br />
* [https://en.wikipedia.org/wiki/Google_China#2006%E2%80%932009:_Censorship_of_Google Google China]<br />
* [https://en.wikipedia.org/wiki/Internet_censorship_in_China Internet censorship in China]<br />
* [https://en.wikipedia.org/wiki/Open_Door_Policy Open Door Policy]<br />
<br />
==References==<br />
<br />
<br />
<br />
[[Category:Privacy]]<br />
[[Category:2020New]]<br />
[[Category:2020Concept]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Surveillance_in_China&diff=91344Surveillance in China2020-03-30T13:47:03Z<p>WikiSysop: </p>
<hr />
<div>Surveillance in China refers to the system of intense observation and monitoring of societal happenings throughout the country of China. The monitoring network is controlled and engineered mostly by government forces, however, recent speculations have presumed the role of private corporations providing their surveillance sources in collaboration with the Chinese government. In recent years, China has exhibited behavior many have framed as a shift to the practices of a dystopian society, including their development and adoption of algorithmic surveillance, a Social Credit System, and the strict China Internet Security Law (Mitchell, 2018). Most notably, China has more cameras installed per person than any other country on Earth, and the number of cameras is growing at a faster rate than the population. In 2018, China had more than 350 million cameras in place throughout the nation, which amounted to about one camera for every 4 Chinese citizens. This figure is expected to rise to more than 560 million cameras by the year 2021 <sup>[6]</sup> (Ricker, 2019).<br />
<br />
=== Algorithmic Surveillance ===<br />
China has integrated a facial recognition technology into its widespread network of surveillance cameras for the purposes of racial profiling. In recent years, China has exhibited harsh behavior in the form of torture, murder, and detainment of the Uighur people, a Muslim minority in the western region of the country. The facial recognition technology has been implemented to track and control the Uighurs through appearance analysis and recording, a pioneering move in the developing world of automated racism. This technology is primarily used by police departments in several cities throughout China, namely some wealthy cities in the eastern region, such as Hangzhou and Wenzhou <sup>[5]</sup> (Mozur, 2019).<br />
<br />
CloudWalk is a Chinese start-up company that has marketed its surveillance system as a method of recognizing sensitive groups of people in the name of neighborhood safety. If the system detected an Uighur living in a neighborhood, and found other Uighurs to have moved into the neighborhood, it would alarm the local authorities of the occurrence. While the system is put in place to recognize Chinese citizens with distinct traits, several environmental factors contribute to the accuracy of the technology, including the lighting of the area and where the cameras are positioned <sup>[5]</sup> (Mozur, 2019).<br />
<br />
=== Social Credit System ===<br />
In 2014, China announced the establishment of a nationwide ranking system that sought to monitor the daily behavior and actions of the population and rank citizens on the basis of "social credit". The purpose of the system is to maintain trust between the population and the government. While the system is expected to be completed by 2020, millions of Chinese citizens are already being used to test the effectiveness of the program. Social scores can fluctuate based on behavior; good behaviors will drive it upwards, while violations, such as driving recklessly, smoking in public areas, or buying unnecessary amenities in excess, will drive the score downwards. The consequences of having a low score can severely limit the abilities of Chinese citizens. For example, people with low scores are currently restricted from purchasing domestic flights to travel throughout China. Low scores can also result in slashed Internet speeds, restricting people from enrollment in better schools or jobs, or public shaming as a "bad citizen". Higher scores can result in discounts on energy bills, better interest rates for bank loans, or having a boosted profile on dating websites. While many have criticized the system for its strict circumstances, others have praised it for its promotion of good behavior <sup>[3]</sup> (Ma, 2018).<br />
<br />
=== Increased Camera Prevalence ===<br />
China is being continuously flooded with camera surveillance units throughout the nation. While a 2018 count of the cameras came to 350 million, this number is set to increase to 560 million within 3 years, slowly increasing the ratio of cameras to people in the country <sup>[6]</sup> (Ricker, 2019). In a recent analysis of national surveillance, eight of the top ten most surveilled cities in the world were found in China. In addition to increasing in prevalence, cameras in the country have been improving in their ability to pinpoint people out of crowds of thousands. A camera recently invented at Fudan University is able to identify people using facial-recognition technology, and can name each person in a stadium of tens of thousands of people. The camera is said to have a resolution that is even more fine-tuned than the human eye. Several of the factories that develop cameras in China collaborate with facial recognition technologies and assign each face with an identification card, tracking the movements and happenings of the people for weeks <sup>[2]</sup> (Cuthbertson, 2019). As the prime source of many of the world's goods, China has also exported millions of surveillance cameras developed in the nation to other countries across the world, sparking concern regarding the safety of the data and potential of Chinese surveillance extending beyond the country's borders <sup>[1]</sup> (Cosgrove, 2019).<br />
<br />
== References ==<br />
# Cosgrove, E. (2019 Dec. 6). One billion surveillance cameras will be watching around the world in 2021, a new study says. CNBC. Retrieved from https://www.cnbc.com/2019/12/06/one-billion-surveillance-cameras-will-be-watching-globally-in-2021.html<br />
# Cuthbertson, A. (2019 Oct. 2). Surveillance camera that can spot someone from crowd of thousands. The Independent. Retrieved from https://www.independent.co.uk/life-style/gadgets-and-tech/news/china-surveillance-camera-facial-recognition-privacy-a9131871.html<br />
# Ma, Alexandra. (2018 Oct. 29). China has started ranking citizens with a creepy 'social credit' system — here's what you can do wrong, and the embarrassing, demeaning ways they can punish you. Business Insider. Retrieved from https://www.businessinsider.com/china-social-credit-system-punishments-and-rewards-explained-2018-4<br />
# Mitchell, Anna. (2018 Feb. 2). China's Surveillance State Should Scare Everyone. The Atlantic. Retrieved from https://www.theatlantic.com/international/archive/2018/02/china-surveillance/552203/<br />
# Mozur, Paul. (2019 Apr. 14). One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority. The New York Times. Retrieved from https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html<br />
# Ricker, Thomas. (2019 Dec. 9). The US, like China, has about one surveillance camera for every four people, says report. The Verge. Retrieved from https://www.theverge.com/2019/12/9/21002515/surveillance-cameras-globally-us-china-amount-citizens<br />
<br />
[[Category:2020New]]<br />
[[Category:2020Concept]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Google_Home&diff=91343Google Home2020-03-30T13:29:15Z<p>WikiSysop: </p>
<hr />
<div>[[File:Intro_google_home.png|right|500px]]<br />
Google Home is a line of smart speakers that uses Google Assistant, a virtual assistant similar to Siri, in order to use voice commands to answer questions asked by the user. It is a smart speaker that can be used to turn a home into a smart home. With the functionality of Spotify, Google Assistant can be asked to play any song by starting the command with “Hey Google” or “OK Google”. <ref name=whatisit>Gebhart, Andrew. [https://www.cnet.com/how-to/everything-you-need-to-know-about-google-home/ "Everything you need to know about Google Home"], "Cnet", 8 May 2019. Retrieved on March 26 2020. </ref>Google Assistant can be asked questions from a normal Google Search such as “What is Barack Obama’s height?” and it would return the information by saying it back to you. The speaker can give information regarding the user's Google Calendar and describe the weather or the distance between points A and B. With the integration of other third party services, a Google Home speaker can return information regarding the temperature in the home or even if there is a stranger at the front door. Google Home has also integrated with its original streaming device the ChromeCast, allowing users to cast something on their TV if a ChromeCast is plugged in by simply talking to their smart speaker device. Google Home has a line of products that are all integrated amongst each other including doorbells, security cameras, and thermostats. These all work with the smart speaker in order to control different aspects of their home. The price range for the speakers and other integrated smart products range from $50 to $150. There are many different types of speakers that all have different functionality including bigger or smaller speakers and some even having a screen display. Alongside this, there are many ethical concerns regarding the speaker's listening functionality reported by many users. <br />
==Types of Speakers==<br />
[[File:Speakers.jpg|right|300px]]<br />
===Nest Mini===<br />
<ref name= types> [https://store.google.com/us/magazine/compare_nest_speakers_displays?toggler3=nest_hub_max&toggler1=home_mini&toggler2=nest_hub Google Store] Retrieved on 26 March 2020 </ref>This is the smallest form of the line of speakers that uses Google Assistant and starts at $50 on Google’s website. This product comes in the shape of a circle and has a microphone on/off switch and two tap features that allow the user to control volume. This speaker has the functionality of working with third party devices, playing music, and is able to answer questions that the normal Google Assistant can answer. This speaker is too small to contain a bass generating woofer. This device is in parallel to Amazon’s Echo Dot, which is Amazon’s smallest smart speaker as well. <br />
<br />
===Home===<br />
The Google Home is one step up from the Nest Mini which has a starting price of $99 and cylindrical in shape. This speaker also has similar functionalities as the mini. It has a microphone on/off switch and tap buttons to control the volume. The speaker is bigger in size than the mini, and provides higher sound quality. The Google Home has a 2 inch driver and dual 2 inch passive radiators. <br />
<br />
===Home Max===<br />
The Google Home Max is the largest speaker in the Google Home category. The price for this is $299 on the Google Store. This device has six microphones allowing it to pick up audio much better than the prior two and has what is known as smart sound. This means that the speaker uses machine learning to adjust the volume of the speaker based on factors like time of day and volume of the room.<br />
<br />
===Nest Hub===<br />
The Google Nest Hub is just like the other smart speakers with a new set of functionalities. The price for this is $129. This speaker contains a screen that provides a visual feedback for certain queries. This is useful for commands such as “what is a recipe for…” and showing who is at the door if a smart doorbell integrated with the speaker. <br />
<br />
===Nest Hub Max===<br />
The Nest Hub Max starts at $229 on the Google Store. This product is similar to the Nest Hub in the sense that it also has a screen. This screen is much bigger and has a Nest camera built in.<br />
<br />
==Origin==<br />
The first modern virtual assistant to be placed on a smartphone was Apple’s Siri which was introduced on the iPhone 4s in 2011. Competition began increasing and the next competitor came from Amazon’s Alexa. Google unveiled their Google assistant in 2016 at the Google I/O Developers conference in May 2016 and was available for the first time on the Google Pixel phone. This led Google to creating a product that with tailored around the virtual assistant and provided a means for consumers to turn their homes into smart homes. <br />
<br />
==Ethical Issues==<br />
The biggest question related to Google Home’s ethical problems is: Is your Google Home always listening to you? <ref name=alwayslistening>Sarkar, Somrata. [https://www.techadvisor.co.uk/feature/digital-home/is-google-home-listening-me-3695908/ "Is Google Home listening to me?"], "Tech Advisor", 20 December 2019. Retrieved on 26 March 2020. </ref> In order to activate a Google Home speaker, the words "OK Google" or "Hey Google" have to be said. A lot of dispute has been made regarding whether this speaker is always listening to you or not. It is argued that, in order for the speaker to recognize the triggering phrase, the speaker has to always be listening and there is no guarantee that anything other than the triggering phrases are not being interpreted. The speakers all have a switch that allow the user to turn the microphone off which is meant to prevent privacy concerns.<br />
<br />
===Privacy Issues at Google===<br />
<br />
There are many privacy concerns related to Google including tailored ads based off of things the user has not even searched online, but has simply spoken about. People wonder if cell phones are always listening to what is being said and are able to interpret and tailor advertisements to that. <ref name=ads>Glaser, April. [https://slate.com/technology/2017/10/googles-home-speakers-are-engineered-to-lock-you-into-googles-ad-empire.html "OK Googe, get out of my house"], "Slate", 5 October 2017. Retrieved on 26 March 2020. </ref> User's biggest concerns related to these speakers are being monitored without realizing they are.<br />
<br />
===Laser Attack===<br />
<br />
[[File:laserhack.jpg|right|300px]]<br />
The laser hack is the most recent ethical concern relating to the line of speakers.<ref name=laser>Goodin, Dan. [https://arstechnica.com/information-technology/2019/11/researchers-hack-siri-alexa-and-google-home-by-shining-lasers-at-them/ "Researchers hack Siri, Alexa, and Google Home by shining lasers at them"], "Ars Technica", 4 November 2019. Retrieved on 26 March 2020. </ref> The Google Home speakers use micro-electro-mechanical systems which unintentionally responds to light as it it were sound. Researchers have exploited this vulnerability by encoding a message in a laser pointer's light frequency that matches the frequency of a spoken message. This light would then have to be shined at the speakers' sensor in order to interpret the message. This attack can be used to unlock smart doors, smart garages, visit websites, locate, unlock, and start smart vehicles if linked to the users Google Account. Though there are many constraints to this including being able to shine a light through a window, lining up the light with the sensor all without having the user notice, the risk of having a speaker being controlled in such a manner still exists.<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category:2020New]]<br />
[[Category:2020Object]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=/r/2meirl4meirl&diff=89473/r/2meirl4meirl2020-03-19T13:10:43Z<p>WikiSysop: </p>
<hr />
<div>[[File:2meirl4meirl.png|10px|framed|The r/2meirl4meirl subreddit photo]]<br />
'''[https://www.reddit.com/r/2meirl4meirl/ /r/2meirl4meirl]''' is a subreddit on [[Reddit]] where users post memes that others can relate to. There is a main focus of self deprecation, edginess, and self degrading humor. This subreddit is self proclaimed as an environment "for relatable posts that are too real for /r/meirl or /r/me_irl."<br />
<ref>https://www.reddit.com/r/2meirl4meirl/</ref><br />
<br />
==Background==<br />
r/2meirl4meirl was created in late April of 2016, and as of now, currently has more than 1.1 million subscribers. As mentioned above, this subreddit was initially created for "when things get too real for meirl." r/meirl and r/me_irl have a combined estimate of 4.5 million subscribers with the latter leading the charge with 3.5 million of those users.<ref>https://subredditstats.com/r/me_irl</ref><br />
<br />
===Differences between 2meirl4meirl and me_irl===<br />
'''[https://www.reddit.com/r/meirl/ /r/meirl]''' was created in late October of 2012, and as mentioned above, has about 3.5 million users. This subreddit is focused on lighthearted relatable memes that aren't viewed as dark or edgy. In fact, r/me_irl has a [https://www.reddit.com/r/me_irl/comments/80ib9u/me_irl_is_not_an_edgy_meme_board/ post] that has been pinned by moderators from two years ago that addresses what the subreddit ''is'' intended for and what it ''is not'' intended for. The post discusses sensitive subjects, such as upsetting news stories and suicide. There is also a locked single comment on the post that describe more of what it means for a post to be edgy and if it meets the required guidelines of the subreddit. To get a better idea of the lighthearted memes on this sub, [https://www.reddit.com/r/me_irl/comments/cz617v/me_irl/ here] is one posted from September of 2019. This relatable, innocent meme currently has a little more than 90 thousand upvotes. [https://www.reddit.com/r/me_irl/comments/ff6xd4/me_irl/ This meme] was posted in early March of 2020 (5 days from the writing of this post) and has already been awarded 82.6 thousand upvotes.<br />
<br />
On the opposite end of the spectrum, there is r/2meirl4meirl. Along with being a tongue twister, it is one of the most popular subs on Reddit right now that has an expertise of self deprecation. Looking through the subreddit, one comes across edgy memes or posts that are somewhat relatable. For example, [https://www.reddit.com/r/2meirl4meirl/comments/cjz4mz/2meirl4meirl/ this] tweet posted on the sub garnered 61.7 thousand upvotes and 402 comments. In a similar vein, [https://www.reddit.com/r/2meirl4meirl/comments/clh1zq/2meirl4meirl/ this] cross post from r/AskReddit gained more than 62 thousand upvotes and 1.1 thousand comments. These memes are able to induce a light release of air through the nose or potentially a dry chuckle if one has that sense of humor, but a majority of this popular subreddit produces edgy memes that in general are detrimental to one's health and well being.<br />
<br />
==Ethical Issues==<br />
===Mental Health and Well Being===<br />
Reddit as a whole has received a lot of criticism for toxic and edgy communities that are not very cognizant of mental health and well being. This ties in with the classic "keyboard warrior" idea of being able to say whatever you want to whoever you want because of the concept of anonymity. In a relatable, but not completely similar vein, subreddits like r/2meirl4meirl foster the romanticization and obsession of mental health problems. There isn't anything necessarily or inherently wrong with this tweet but it is designed to encourage one of the 62 thousand users who liked [https://www.reddit.com/r/2meirl4meirl/comments/c4zlla/2meirl4meirl/ this] post to say "Oh me too! This is so funny and relatable!" As far as mental well being goes, it definitely is important that these subreddits normalize and understand the issues behind mental health. However, users of this sub seem a little too interested in bathing in their horrible mental health and basking in it to gain the attention of the internet. Posts like [https://www.reddit.com/r/2meirl4meirl/comments/ea6rur/2meirl4meirl/ this] are designed to be funny and over the top but in reality, they are just depressing and discouraging.<br />
[[File:SadMeme.jpg|thumbnail|right|This post is a perfect example of self deprecating humor that is "too real".]]<br />
===Reddit as an Echo Chamber===<br />
Posts on these subreddits and the communities themselves encourage self deprecating memes and a lack of self care. The problem of echo chambers has been recurring since the dawn of technological times and it's no surprise that it is a vital part of what keeps people coming back to these subs, upvoting and commenting on these memes designed to make one feel bad but also like they are not alone in their struggle. This meme to the right with over 61 thousand upvotes is a perfect representation of this, with the top comments telling the same story. Comments such as "I felt that" and "same, so disposable" gained 69 and 256 upvotes respectively, with more in depth comments about being "that friend" received around 2 thousand upvotes.<ref>https://www.reddit.com/r/2meirl4meirl/comments/el18es/2meirl4meirl/</ref> This kind of mentality is soul crushing and it seems almost impossible to escape the echo chamber.<br />
<br />
These issues are very real and very important in terms of ethicality. Not just when it comes to ethics in technology, but also in terms of self ethics. In this scenario, the technology and the subreddits are pipelines that blur and obscure the ethics of these users. <br />
<br />
==Similar Subreddits==<br />
===[https://www.reddit.com/r/2meirl42meirl4meirl/ r/2meirl42meirl4meirl]===<br />
If you thought r/2meirl4meirl was a mouthful, get ready for this monstrosity. '''Warning: this subreddit is extremely insensitive and makes light of many serious issues, take caution in browsing it.''' Created in January of 2016, with almost 150 thousand members, this subreddit is a festering breeding ground for edginess and self hatred. Although this sub has a pinned post about reaching out if you actually need help,<ref>https://www.reddit.com/r/2meirl42meirl4meirl/comments/6uaoox/friendly_reminder_this_is_not_a_subreddit_for/</ref> practically every post in this community revolves around jokes about suicide, self-disdain, and lack of neurotransmitters. To make matters worse, the comments on these posts are just as gruesome, if not more.<br />
===[https://www.reddit.com/r/depression/ r/depression]===<br />
Although this is not nearly as extreme as the other two subs mentioned above, r/depression is not what many people think it would be. r/depression advertises itself as a supportive space, but many of the 614 thousand members of this subreddit use this platform as a way of blowing of steam. It is true that occasional posts receive a lot of feedback and point users in the right direction for '''real''' help, but a majority of the posts on this subreddit are people venting about their mental health issues and comments reciprocating those feelings. [https://www.reddit.com/r/depression/comments/fi3i3f/this_subreddit_is_not_helpful/ This linked post] provides a good idea of what this sub really does. Mental health subreddits such as these may do more hard than good and cross into a very gray area in terms of ethicality.<br />
==References==<br />
<br />
[[Category:2020New]]<br />
[[Category:2020Object]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=/r/2meirl4meirl&diff=89472/r/2meirl4meirl2020-03-19T13:09:31Z<p>WikiSysop: </p>
<hr />
<div>[[File:2meirl4meirl.png|10px|framed|The r/2meirl4meirl subreddit photo]]<br />
'''[https://www.reddit.com/r/2meirl4meirl/ /r/2meirl4meirl]''' is a subreddit on [[Reddit]] where users post memes that others can relate to. There is a main focus of self deprecation, edginess, and self degrading humor. This subreddit is self proclaimed as an environment "for relatable posts that are too real for /r/meirl or /r/me_irl."<br />
<ref>https://www.reddit.com/r/2meirl4meirl/</ref><br />
<br />
==Background==<br />
r/2meirl4meirl was created in late April of 2016, and as of now, currently has more than 1.1 million subscribers. As mentioned above, this subreddit was initially created for "when things get too real for meirl." r/meirl and r/me_irl have a combined estimate of 4.5 million subscribers with the latter leading the charge with 3.5 million of those users.<ref>https://subredditstats.com/r/me_irl</ref><br />
<br />
===Differences between 2meirl4meirl and me_irl===<br />
'''[https://www.reddit.com/r/meirl/ /r/meirl]''' was created in late October of 2012, and as mentioned above, has about 3.5 million users. This subreddit is focused on lighthearted relatable memes that aren't viewed as dark or edgy. In fact, r/me_irl has a [https://www.reddit.com/r/me_irl/comments/80ib9u/me_irl_is_not_an_edgy_meme_board/ post] that has been pinned by moderators from two years ago that addresses what the subreddit ''is'' intended for and what it ''is not'' intended for. The post discusses sensitive subjects, such as upsetting news stories and suicide. There is also a locked single comment on the post that describe more of what it means for a post to be edgy and if it meets the required guidelines of the subreddit. To get a better idea of the lighthearted memes on this sub, [https://www.reddit.com/r/me_irl/comments/cz617v/me_irl/ here] is one posted from September of 2019. This relatable, innocent meme currently has a little more than 90 thousand upvotes. [https://www.reddit.com/r/me_irl/comments/ff6xd4/me_irl/ This meme] was posted in early March of 2020 (5 days from the writing of this post) and has already been awarded 82.6 thousand upvotes.<br />
<br />
On the opposite end of the spectrum, there is r/2meirl4meirl. Along with being a tongue twister, it is one of the most popular subs on Reddit right now that has an expertise of self deprecation. Looking through the subreddit, one comes across edgy memes or posts that are somewhat relatable. For example, [https://www.reddit.com/r/2meirl4meirl/comments/cjz4mz/2meirl4meirl/ this] tweet posted on the sub garnered 61.7 thousand upvotes and 402 comments. In a similar vein, [https://www.reddit.com/r/2meirl4meirl/comments/clh1zq/2meirl4meirl/ this] cross post from r/AskReddit gained more than 62 thousand upvotes and 1.1 thousand comments. These memes are able to induce a light release of air through the nose or potentially a dry chuckle if one has that sense of humor, but a majority of this popular subreddit produces edgy memes that in general are detrimental to one's health and well being.<br />
<br />
==Ethical Issues==<br />
===Mental Health and Well Being===<br />
Reddit as a whole has received a lot of criticism for toxic and edgy communities that are not very cognizant of mental health and well being. This ties in with the classic "keyboard warrior" idea of being able to say whatever you want to whoever you want because of the concept of anonymity. In a relatable, but not completely similar vein, subreddits like r/2meirl4meirl foster the romanticization and obsession of mental health problems. There isn't anything necessarily or inherently wrong with this tweet but it is designed to encourage one of the 62 thousand users who liked [https://www.reddit.com/r/2meirl4meirl/comments/c4zlla/2meirl4meirl/ this] post to say "Oh me too! This is so funny and relatable!" As far as mental well being goes, it definitely is important that these subreddits normalize and understand the issues behind mental health. However, users of this sub seem a little too interested in bathing in their horrible mental health and basking in it to gain the attention of the internet. Posts like [https://www.reddit.com/r/2meirl4meirl/comments/ea6rur/2meirl4meirl/ this] are designed to be funny and over the top but in reality, they are just depressing and discouraging.<br />
[[File:SadMeme.jpg|thumbnail|right|This post is a perfect example of self deprecating humor that is "too real".]]<br />
===Reddit as an Echo Chamber===<br />
Posts on these subreddits and the communities themselves encourage self deprecating memes and a lack of self care. The problem of echo chambers has been recurring since the dawn of technological times and it's no surprise that it is a vital part of what keeps people coming back to these subs, upvoting and commenting on these memes designed to make one feel bad but also like they are not alone in their struggle. This meme to the right with over 61 thousand upvotes is a perfect representation of this, with the top comments telling the same story. Comments such as "I felt that" and "same, so disposable" gained 69 and 256 upvotes respectively, with more in depth comments about being "that friend" received around 2 thousand upvotes.<ref>https://www.reddit.com/r/2meirl4meirl/comments/el18es/2meirl4meirl/</ref> This kind of mentality is soul crushing and it seems almost impossible to escape the echo chamber.<br />
<br />
These issues are very real and very important in terms of ethicality. Not just when it comes to ethics in technology, but also in terms of self ethics. In this scenario, the technology and the subreddits are pipelines that blur and obscure the ethics of these users. <br />
<br />
==Similar Subreddits==<br />
===[https://www.reddit.com/r/2meirl42meirl4meirl/ r/2meirl42meirl4meirl]===<br />
If you thought r/2meirl4meirl was a mouthful, get ready for this monstrosity. '''Warning: this subreddit is extremely insensitive and makes light of many serious issues, take caution in browsing it.''' Created in January of 2016, with almost 150 thousand members, this subreddit is a festering breeding ground for edginess and self hatred. Although this sub has a pinned post about reaching out if you actually need help,<ref>https://www.reddit.com/r/2meirl42meirl4meirl/comments/6uaoox/friendly_reminder_this_is_not_a_subreddit_for/</ref> practically every post in this community revolves around jokes about suicide, self-disdain, and lack of neurotransmitters. To make matters worse, the comments on these posts are just as gruesome, if not more.<br />
===[https://www.reddit.com/r/depression/ r/depression]===<br />
Although this is not nearly as extreme as the other two subs mentioned above, r/depression is not what many people think it would be. r/depression advertises itself as a supportive space, but many of the 614 thousand members of this subreddit use this platform as a way of blowing of steam. It is true that occasional posts receive a lot of feedback and point users in the right direction for '''real''' help, but a majority of the posts on this subreddit are people venting about their mental health issues and comments reciprocating those feelings. [https://www.reddit.com/r/depression/comments/fi3i3f/this_subreddit_is_not_helpful/ This linked post] provides a good idea of what this sub really does. Mental health subreddits such as these may do more hard than good and cross into a very gray area in terms of ethicality.<br />
==References==<br />
<br />
[[category: <br />
[[category</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Content_moderation&diff=89440Content moderation2020-03-17T16:56:40Z<p>WikiSysop: </p>
<hr />
<div>[[File:Moderation workflow.png|600px|thumbnail|Archetypal Content Moderation Workflow]]<br />
{{Nav-Bar|Topics#A}}<br><br />
'''Content moderation''' is the process of monitoring, filtering and removing online user-generated content according to the rules of a private organization or the regulations of a government. It is used to restrict illegal or obscene content, [[spam]], and content considered offensive or incongruous with the values of the moderator. This is done to protect the general public and to abide by generally accepted cultural practices and norms. When applied to dominant platforms with significant influence, content moderation may be conflated with [[censorship]]. Ethical issues involving content moderation include the psychological effects on content moderators, human and algorithmic bias in moderation, the trade-off between free speech and free association, and the impact of content moderation on minority groups.<br />
<br />
==Overview==<br />
<br />
Most types of moderation involve a top-down approach, where a moderator or small group of moderators are given discretionary power by a platform to approve or disapprove user-generated content. These moderators may be paid contractors or unpaid volunteers. A moderation hierarchy may exist or each moderator may have independent and absolute authority to make decisions. <br />
<br />
In general, content moderation can be broken down into 6 major categories:<ref>Grime-Viort, Blaise (December 7, 2010). [https://www.socialmediatoday.com/content/6-types-content-moderation-you-need-know-about "6 Types of Content Moderation You Need to Know About"]. ''Social Media Today.'' Retrieved March 26, 2019.</ref><br />
<br />
* '''Pre-Moderation''' screens each submission before it is visible to the public. This creates a bottleneck in user-engagement, and the delay may cause frustration in the user-base. However, it ensures maximum protection against undesired content, eliminating the risk of exposure to unsuspecting users. It is only practical for small user communities otherwise the flow of content would be slowed down too much. It was common in moderated newsgroups on Usenet.<ref>''Big-8.org''. August 4, 2012. [https://archive.is/t2xJ "Moderated Newsgroups"]. Archived from [http://www.big-8.org/wiki/Moderated_Newsgroups#Who_can_force_the_moderators_to_obey_the_group_charter.3F the original] on August 4, 2012. Retrieved March 26, 2019.</ref> Pre-moderation provides a high control of what ends up visible to the public. This method is suited towards communities where child protection is vital. <br />
<br />
* '''Post-Moderation''' screens each submission after it is visible to the public. While preventing the bottleneck problem, it is still impractical for large user communities due to the vast number of submissions. Furthermore, as the content is often reviewed in a queue, undesired content may remain visible for an extended period of time, drowned out by benign content ahead of it, which must still be reviewed. This method is preferable to pre-moderation from a user-experience perspective, since the flow of content has not been slowed down by waiting for approval. <br />
<br />
* '''Reactive moderation''' reviews only that content which has been flagged by users. It retains the benefits of both pre- and post-moderation, allowing for real-time user-engagement and the immediate review of only potentially undesired content. However, it is reliant on user participation and is still susceptible to benign content being falsely flagged. Therefore it is most practical for large user communities which have a lot of user activity. Most modern social media platforms, including [[Facebook]] and [[YouTube]], rely on this method. This method allows for the users themselves to be held accountable for any information available and for determining what should or should not be taken down. This method is more easily scalable to a large number of users than both pre and post-moderation.<br />
<br />
* '''Distributed moderation''' is an exception to the top-down approach. It instead gives the power of moderation to the users, often making use of a voting system. This is common on [[Reddit]] and Slashdot, the latter also using a meta-moderation system, in which users also rate the decisions of other users.<ref>''Slashdot''. Retrieved March 26, 2019.[https://slashdot.org/faq/metamod.shtml "Moderation and Metamoderation"]. </ref> This method scales well across user-communities of all sizes, but also relies on users having the same perception of undesired content as the platform. It is also susceptible to groupthink and malicious coordination, also known as brigading.<ref>''Reddit''. January 18, 2018. Retrieved March 26, 2019.[https://www.reddit.com/wiki/reddiquette#wiki_in_regard_to_voting "Reddiquette: In Regard to Voting"] </ref><br />
<br />
* '''Automated moderation''' is the use of software to automatically assess content for desirability. It can be used in conjunction with any of the above moderation types. Its accuracy is dependent on the quality of its implementation, and it is susceptible to algorithmic bias and adversarial examples<ref>Goodfellow, Ian; Papernot, Nicolas; et al (February 24, 2017). [https://openai.com/blog/adversarial-example-research/ "Attacking Machine Learning with Adversarial Examples"]. ''OpenAI''. Retrieved March 26, 2019.</ref>. Copyright detection software on YouTube and spam filtering are examples of automated moderation<ref name="Injustice">Tassi, Paul (December 19, 2013). [https://www.forbes.com/sites/insertcoin/2013/12/19/the-injustice-of-the-youtube-content-id-crackdown-reveals-googles-dark-side/#5762095b66c8 "The Injustice of the YouTube Content ID Crackdown Reveals Google's Dark Side"]. ''Forbes''. Retrieved March 26, 2019.</ref>.<br />
<br />
* '''No moderation''' is the lack of moderation entirely. Such platforms are often hosts to illegal and obscene content, and typically operate outside the law, such as The Pirate Bay and [[Dark Web]] markets. Spam is a perennial problem for unmoderated platforms, but may be mitigated by other methods, such as limited posting frequency and monetary barriers to entry. However, small communities with shared values and few bad actors can also thrive under no moderation, like unmoderated Usenet newsgroups.<br />
<br />
==History==<br />
<br />
===Pre-1993: Usenet and the Open Internet===<br />
<br />
Usenet emerged in the early 1980s as a network of university and private computers, and quickly became the world's first Internet community. The decentralized platform hosted a collection of message boards known as newsgroups. These newsgroups were small communities by modern standards, and consisted of like-minded, technologically-inclined users sharing the hacker ethic. This collection of principles, including "access to computers should be unlimited", "mistrust authority: promote decentralization", and "information wants to be free", created a culture that was resistant to moderation and free of top-down censorship.<ref name="Hackers">Levy, Steven (2010). "Chapter 2: The Hacker Ethic". ''[https://classes.visitsteve.com/hacking/wp-content/Steven-Levy-Hackers-ch1+2.pdf Hackers: Heroes of the Computer Revolution]''. pp. 27-31. ISBN 978-1-449-38839-3. Retrieved March 26, 2019.</ref> The default assumptions were of users acting in good faith and that new users could be gradually assimilated into the shared culture. As a result, only a minority of newsgroups were moderated, most allowing anyone to post however they pleased, as long as they followed the community's social norms, known as "netiquette."<ref name="Art of Internet">Kehoe, Brendan P. (January 1992). "4. Usenet News". [https://www.cs.indiana.edu/docproject/zen/zen-1.0_6.html ''Zen and the Art of the Internet'']. Retrieved March 26, 2019.</ref> Furthermore, the Internet, in general, was considered separate and distinct from the physical space its servers were located, existing in its own "cyberspace" not subject to the will of the state. Throughout this era of the Open Internet, online activity mostly escaped the notice of government regulation, creating a policy gap that only began to close in the late 1990s.<ref name="Open Internet">Palfrey, John (2010). "Four Phases of Internet Regulation". ''Social Research''. '''77''' (3): 981-996. Retrieved from http://www.jstor.org/stable/40972303 on March 26, 2019.</ref><br />
<br />
===1994 - 2005: Eternal September and Growth===<br />
<br />
In September 1993, AOL began offering Internet access to the general public. The resulting flood of users arrived too quickly and in too many numbers to be assimilated into the existing culture, and the shared values that had allowed unmoderated newsgroups to flourish were lost. This was known as the Eternal September, and the resulting growth transformed the Internet from a high-trust to a low-trust community.<ref>Koebler, Jason (September 30, 2015). [https://motherboard.vice.com/en_us/article/nze8nb/its-september-forever "It's September Forever"]. ''Motherboard''. Retrieved March 26, 2019.</ref> The consequences of this transformation were first seen in 1994, when the first recorded instance of [[spam]] was sent out across Usenet.<ref>Everett-Church, Ray (April 13, 1999). [https://www.wired.com/1999/04/the-spam-that-started-it-all/ "The Spam That Started It All"]. ''Wired''. Retrieved March 26, 2019.</ref> The spam outraged Usenet users, and the first anti-spam bot was created in response, ushering in the era of content moderation.<ref>Gulbrandsen, Arnt (October 12, 2009). [http://rant.gulbrandsen.priv.no/canter-siegel "Canter & Siegel: What actually happened"]. Retrieved March 26, 2019.</ref><br />
<br />
With the invention of the [https://en.wikipedia.org/wiki/World_Wide_Web World Wide Web], users began to drift away from Usenet, while thousands of [[Internet forum|forums]] and blogs emerged as replacements. These small communities were often overseen by single individuals or small teams, and exercised total moderating control over their domains. In response to the growth of spam and other bad actors, these often had much stricter rules than early Usenet groups. However, the vast marketplace of available forums and places of discussion was such that, if a user did not like the moderation policies in one platform, they could easily move to another. <br />
<br />
As corporate platforms matured, they began to adopt limited content policies as well, though in a more ad-hoc manner. In 2000, Compuserve was the first platform to develop an "Acceptable Use" policy, which banned racist speech<ref name="Secret History">Buni, Catherine; Chemaly, Soraya (March 13, 2016). [https://www.theverge.com/2016/4/13/11387934/internet-moderator-history-youtube-facebook-reddit-censorship-free-speech "The Secret Rules of the Internet"]. ''The Verge''. Retrieved March 26, 2019.</ref> eBay soon followed in 2001, banning the sale of hate memorabilia and propaganda.<ref>Cox, Beth (May 3, 2001). [http://www.internetnews.com/ec-news/article.php/758221/eBay+Bans+Nazi+Hate+Group+Memorabilia.htm "eBay Bans Nazi, Hate Group Memorabilia"]. ''Internet News''. Retrieved March 26, 2019.</ref><br />
<br />
===2006 - 2010: Social Media and Early Corporate Moderation===<br />
[[File:Fbmoderation.jpg|400px|thumbnail|Facebook Content Moderation Office]]<br />
In the mid-2000s, social media platforms such as [[YouTube]], [[Twitter]], [[Tumblr]], [[Reddit]], and [[Facebook]] began to emerge, and quickly became dominant, centralized platforms that gradually displaced the multitude of blogs and message boards as a place for user discussion. These platforms initially struggled with content moderation. YouTube in particular developed ad-hoc policies from individual cases, gradually building up an internal set of rules that was opaque, arbitrary, and difficult for moderators to apply.<ref name="Secret History"></ref><ref name="Gatekeepers">Rosen, Jeffrey (November 28, 2008). [https://www.nytimes.com/2008/11/30/magazine/30google-t.html "Google's Gatekeepers"]. ''New York Times''. Retrieved March 26, 2019.</ref><br />
<br />
Other platforms, such as [[Twitter]] and Reddit adopted the unmoderated, free speech ethos of old, with Twitter claiming to be the "free speech wing of the free speech party" and Reddit stating that "distasteful" subreddits would not be removed, "even if we find it odious or if we personally condemn it."<ref>'The Guardian''. Halliday, Josh (March 22, 2012). [https://www.theguardian.com/media/2012/mar/22/twitter-tony-wang-free-speech "Twitter's Tony Wang: 'We are the free speech wing of the free speech party'"]. Retrieved March 26, 2019.</ref><ref>''BBC News'' October 17, 2012. [https://www.bbc.com/news/technology-19975375 "Reddit will not ban 'distasteful' content, chief executive says"]. Retrieved March 26, 2019.</ref><br />
<br />
===2010 - Present: Centralization and Expanded Moderation===<br />
<br />
Throughout the 2010s, as social media platforms became ubiquitous, the ethics of their moderation policies were brought into question. As these centralized platforms began to have significant influence over national and international discourse, concerns were raised over the presence of offensive content as well as the stifling of expression. <ref name="Impossible Choices">Masnick, Mike (August 9, 2019). [https://www.techdirt.com/articles/20180808/17090940397/platforms-speech-truth-policy-policing-impossible-choices.shtml "Platforms, Speech and Truth: Policy, Policing and Impossible Choices"]. ''Techdirt''. Retrieved March 26, 2019.</ref><ref name="Garbage">Jeong, Sarah (2018). [https://cdn.vox-cdn.com/uploads/chorus_asset/file/12599893/The_Internet_of_Garbage.0.pdf ''The Internet of Garbage'']. ISBN 978-0-692-18121-8. Retrieved March 26, 2019.</ref> Additionally, internet infrastructure providers also began to remove content hosted on their platforms. <br />
<br />
In 2010, [[WikiLeaks]] leaked the US Diplomatic Cables and hosted the documents on [[Amazon.com|Amazon]] Web Services. These were later removed by Amazon as against their content policies. WikiLeaks' DNS provider also made the decision to drop their website, effectively removing WikiLeaks from the Internet until an alternative host could be found.<ref>Arthur, Charles; Halliday, Josh (December 3, 2010). [https://www.theguardian.com/media/blog/2010/dec/03/wikileaks-knocked-off-net-dns-everydns "WikiLeaks fights to stay online after US company withdraws domain name"]. ''The Guardian''. Retrieved March 26, 2019.</ref><br />
<br />
In 2012, Reddit user /u/violentacrez was [[Doxxing|doxxed]] by Gawker Media for moderating several controversial subreddits, including /r/Creepshots. The subsequent media spotlight caused Reddit to reconsider their minimalist approach to content moderation.<ref>Boyd, Danah (October 29, 2012). [https://www.wired.com/2012/10/truth-lies-doxxing-internet-vigilanteism/ "Truth, Lies and 'Doxing": The Real Moral of the Gawker/Reddit Story"]. ''Wired''. Retrieved March 26, 2019.</ref> This set a precedent which was used to ban more subreddits over the next few years. In 2015, Reddit banned /r/FatPeopleHate, which marked a turning point at which Reddit no longer considered itself a "bastion of free speech."<ref>[https://www.reddit.com/r/announcements/comments/39bpam/removing_harassing_subreddits/ "Removing Harassing Subreddits"]. June 10, 2015. ''Reddit''. Retrieved March 26, 2019.</ref> In 2019, Reddit banned /r/WatchPeopleDie, in an effort to suppress the spread of the Christchurch mass shooting video, a move widely considered as [[censorship]]. <ref>Hatmaker, Taylor (March 15, 2019). [https://techcrunch.com/2019/03/15/reddit-watchpeopledie-subreddit-gore/ "After Christchurch, Reddit bans communities infamous for sharing graphic videos of death"]. ''TechCrunch''. Retrieved March 26, 2019.</ref><br />
<br />
In 2015, [[Instagram]] came under fire for moderating female nipples, but not male nipples. It was later revealed that this decision was in turn due to content moderation policies for apps in Apple App Store.<ref>Kleeman, Sophie (October 1, 2015). [https://mic.com/articles/126137/instagram-banned-nipples-because-of-apple#.2IxeNH5Lr "Instagram Finally Revealed the Reason It Banned Nipples - It's Apple"]. ''Mic''. Retrieved March 26, 2019.</ref><br />
<br />
In 2016, in the aftermath of [[Gamergate]] and it's associated harassment, Twitter instituted the Trust and Safety Council, and began enforcing stricter moderation policies on their users.<ref>Cartes, Patricia (February 9, 2016). [https://blog.twitter.com/en_us/a/2016/announcing-the-twitter-trust-safety-council.html "Announcing the Twitter Trust & Safety Council"]. ''Twitter''. Retrieved March 26, 2019.</ref> In 2019, Twitter was heavily criticized for political bias, inconsistency and lack of transparency in their moderation practices.<ref>Rogan, Joe (March 5, 2019). [http://podcasts.joerogan.net/podcasts/jack-dorsey-vijaya-gadde-tim-pool "Jack Dorsey, Vijaya Gadde, & Tim Pool"]. ''Joe Rogan Experience'' (Podcast). Retrieved March 26, 2019.</ref> <br />
<br />
In 2018, Tumblr banned all adult content from its platform. This resulted in the mass removal of LGBT and GSM support groups and communities.<ref>Ho, Vivian (December 4, 2018). [https://www.theguardian.com/technology/2018/dec/03/tumblr-adult-content-ban-lgbt-community-gender "Tumblr's adult content ban dismays some users: 'It was a safe space'"]. ''The Guardian''. Retrieved March 26, 2019.</ref><br />
<br />
In 2019, politicians pushed for content regulation from Google and [[Facebook]], specifically regarding online hate speech. The increase in terrorist attacks and real-world violence has caused concern that these "tech giants have become digital incubators for some of the most deadly, racially motivated attacks around the world," including the white-supremacist attack in VA and the synagogue shooting in PA. <ref>Romm, T. (2019) The Washington Post. https://www.washingtonpost.com/technology/2019/04/08/facebook-google-be-quizzed-white-nationalism-political-bias-congress-pushes-dueling-reasons-regulation/?noredirect=on&utm_term=.e0dfe2d9dea7</ref> The mosque attack in Christchurch, New Zealand is an example of why the urge for regulation has become so prominent as the attacker live-recorded the shooting on Facebook. Facebook will reexamine its current procedures for identifying violent content and quickly taking action to remove it. <ref>Shaban, H. (2019). Facebook to reexamine how live stream videos are flagged after Christchurch shooting. The Washington Post. https://www.washingtonpost.com/technology/2019/03/21/facebook-reexamine-how-recently-live-videos-are-flagged-after-christchurch-shooting/?utm_term=.a604cc1428b8</ref><br />
<br />
==Ethical Issues==<br />
The ethical issues regarding content moderation include how it is carried out, the possible bias of such content moderators, and the negative effects this kind of job has on moderators. The problem lies in the fact that content moderation cannot be carried out by an autonomous program since many cases are highly nuanced and detectable only by knowing the context and the way humans might perceive it. Not only is this job often ill-defined in terms of policy, content moderators are often expected to make very difficult judgments while being afforded very few to no mistakes.<br />
<br />
===Virtual Sweatshops===<br />
Often, companies outsource their content moderation tasks to third parties. This work cannot be done by computer algorithms because it is often very nuanced, which is where [http://si410wiki.sites.uofmhosting.net/index.php/Virtual_sweatshops virtual sweatshops] enter the picture. Virtual sweatshops enlist workers to complete mundane tasks in which they will receive small monetary reward for their labor. While some view this as a new market for human labor with extreme flexibility, there are also concerns with labor laws. There is not yet policy that exists on work regulations for internet labor, requiring teams of people overseas who are underpaid for the labor they perform. Companies overlook and often choose not to acknowledge the hands-on effort they require. Human error is inevitable causing concerns with privacy and trust when information is sent to these third-party moderators. <ref> Zittrain, Jonathon. "THE INTERNET CREATES A NEW KIND OF SWEATSHOP." NewsWeek. December 7, 2009. https://www.newsweek.com/internet-creates-new-kind-sweatshop-75751 </ref><br />
<br />
=== Google's Content Moderation & the Catsouras Scandal ===<br />
[https://en.wikipedia.org/wiki/Google Google] is home to a practically endless amount of content and information all of which is for the most part, not regulated. In 2006, a young teen in Southern California named Nikki Catsouras crashed her car, which resulted in her gruesome death and decapitation. On the scene, members of the police force were tasked with taking pictures of the scene. However, as a Halloween joke, a few of the members who took the photos sent them around to various friends and family members. The picture of Nikki's mutilated body was then passed around the internet and was easily accessible via Google. The Catsouras family was devastated that these pictures of their daughter were being seen and viewed by millions, and desperately attempted to get the photo removed from the Google platform. However, Google refused to comply with Catsouras plea. This is a clear ethical dilemma that involves content moderation as this picture was certainly not meant to be released to the public and was very difficult for the family, but because Google did not want to begin moderating specific content of their platform they did nothing. This brings up the ethical question of if people have "The Right to Be Forgotten" <ref> Toobin, Jeffrey. “The Solace of Oblivion.” The New Yorker, 22 Sept. 2014, www.newyorker.com/.</ref>.<br />
<br />
Another massive ethical issue with the moderation of content online is the fact that the owners of the content or platform decide what is and what is not moderated. Thousands of people and companies claim that Google purposefully moderates content that directly competes with their platform. Shivaun Moeran and Adam Raff are two computer scientists who together created an incredibly powerful search platform called Foundem.com. The website was helpful for finding any amounts of information, it was particularly helpful for finding the cheapest possible items being sold on the internet. The key to the site was a Vertical Search Algorithm, which as an incredibly complex computer algorithm that focuses on search. This vertical search algorithm was significantly more powerful than Google's search algorithm, which was a horizontal search algorithm. The couple posted their site and within the first few days experienced great success and many site visitors, however, after a few days the visitor rate significantly decreased. They discovered that their site had been pushed multiple pages back on Google. This is because it directly competed with the "Google Shopping" app that had been released by Google. Morean and Raff had countless lawsuits filed and met with people at Google and other large companies to figure out what the issue was and how they could get it fixed but were met with silence or ambiguity. Foundem.com never became the next big search algorithm, partly because of the ethical issues seen in content moderation by Google <ref> Duhigg, Charles. “The Case Against Google.” The New York Times, The New York Times, 20 Feb. 2018, www.nytimes.com/. </ref><br />
<br />
===Psychological Effects on Moderators===<br />
Content moderation can have significant negative effects on the individuals tasked with carrying out the motivation. Because most content must be reviewed by a human, professional content moderators spend hours every day reviewing disturbing images and videos, including pornography (sometimes involving children or animals) gore, executions, animal abuse, and hate speech. Viewing such content repeatedly, day after day can be stressful and traumatic, with moderators sometimes developing PTSD-like symptoms. Others, after continuous exposure to fringe ideas and conspiracy theories, develop intense paranoia and anxiety, and begin to accept those fringe ideas as true.<ref name="Secret History"></ref><ref name="Beheadings">Chen, Adrian (October 23, 2014). [https://www.wired.com/2014/10/content-moderation/ "The Laborers Who Keep Dick Pics and Beheadings Out of Your Facebook Feed"] ''Wired''. Retrieved March 26, 2019.</ref><ref name="Trauma">Newton, Casey (February 25, 2019). [https://www.theverge.com/2019/2/25/18229714/cognizant-facebook-content-moderator-interviews-trauma-working-conditions-arizona "The Trauma Floor: The Secret Lives of Facebook Moderators in America"] ''The Verge''. Retrieved March 26, 2019.</ref><br />
<br />
Further negative effects are brought on by the stress of applying the subjective and inconsistent rules regarding content moderation. Moderators are often called upon to make judgment calls regarding ambiguously-objectionable material or content that is offensive but breaks no rules. However, the performance of their moderation decisions is strictly monitored and measured against the subjective judgment calls of other moderators. A few mistakes are all it takes for a professional moderator to lose their job.<ref name="Trauma"></ref><br />
<br />
A report detailing the lives of [[Facebook]] content moderators explained the poor conditions these workers are subject to <ref name = "facebook">Simon, Scott, and Emma Bowman. “Propaganda, Hate Speech, Violence: The Working Lives Of Facebook's Content Moderators.” NPR, NPR, 2 Mar. 2019, www.npr.org/2019/03/02/699663284/the-working-lives-of-facebooks-content-moderators.</ref>. Even though content moderators have an emotionally intense, stressful job they are often underpaid. In addition, Facebook does provide forms of counseling to their employees, however, many are dissatisfied with the service <ref name = "facebook"/>. The employees review a significant amount of traumatizing information daily, but it is their responsibility to seek therapy if needed, which is difficult for many. They are also required to constantly oversee content and are only allotted two 15 minute breaks and a half an hour lunch break. In the cases where they review particularly horrifying content, they are only given a nine minute break to recover <ref name = "facebook"/>. [[Facebook]] is often criticized for the ethical treatment of their content moderator employees.<br />
<br />
===Information Transparency===<br />
<br />
Information transparency is the degree to which information about a system is visible to its users.<ref>Turilli, Matteo; Floridi, Luciano (March 10, 2009). [https://link.springer.com/article/10.1007/s10676-009-9187-9 "The ethics of information transparency"] ''Ethics and Information Technology''. '''11''' (2): 105-112. doi:[https://doi.org/10.1007/s10676-009-9187-9<br />
10.1007/s10676-009-9187-9<br />
]. Retrieved March 26, 2019.</ref> By this definition, content moderation is not transparent at any level. First, content moderation is often not transparent to the public, those it is trying to moderate. While a platform may have public rules regarding acceptable content, these are often vague and subjective, allowing the platform to enforce them as broadly or as narrowly as it chooses. Furthermore, such public documents are often supplemented by internal documents accessible only to the moderators themselves.<ref name="Secret History"></ref><br />
<br />
Content moderation is not transparent at the level of moderators either. The internal documents are often as vague as the public ones and contain significantly more internal inconsistencies and judgment calls that make them difficult to apply fairly. Furthermore, such internal documents are often contradicted by statements from higher-ups, which in turn may be contradicted by similar statements.<ref name="Trauma"></ref><br />
<br />
Finally, even at the corporate level where policy is set, moderation is not transparent. Moderation policies are often created by an ad-hoc, case-by-case process and applied in the same manner. Some content that would normally be removed by moderation rules will be accepted for special circumstances, such as "newsworthiness". For example, videos of violent government suppression could be displayed or not, depending on the whims of moderation policy-makers and moderation QAs at the time.<ref name="Secret History"></ref><br />
<br />
===Bias===<br />
<br />
Due to its inherently subjective nature, content moderation can suffer from various kinds of bias. Algorithmic bias is possible when automated tools are used to remove content. For example, YouTube's automated Content ID tools may flag reviews of films or games that feature clips or gameplay as copyright violations, despite being Fair Use when used to criticize<ref name="Injustice"></ref>. When a youtube content is flagged they lose out on any ad revenue from that video during the time their content is flagged. Even if a content creator is able to fight the claim and has their video unflagged by Youtube they don't receive any of the revenue from their video while it was flagged. The algorithm bias thus serious financial effects for creators and especially for small channel who can't afford to fight the copyright claim <ref name = "Romano"> Romano, Aja. “YouTube's ‘Ad-Friendly’ Content Policy May Push One of Its Biggest Stars off the Website.” Vox, Vox, 2 Sept. 2016, www.vox.com/2016/9/2/12746450/youtube-monetization-phil-defranco-leaving-site.</ref>. Moderation may also suffer from cultural bias, when something considered objectionable by one group may be considered fine to another. For example, moderators tasked with removing content that depicts minors engaging in violence may disagree over what constitutes a minor. Classification of obscenity is also culturally biased, with different societies around the world having different standards of modesty.<ref name="Secret History"></ref><ref name="Gatekeepers"></ref> Moderation, both from the perspective of humans and automated systems, may be inherently flawed in that the subjective nature that comes along with deciding what is right versus what is wrong can be difficult to lay out in concrete terms. While there is no uniform solution to issues of bias in content moderation, some have suggested that approaching these issues with a utilitarian approach may serve as guiding ethical standard. <ref>Mandal, Jharna, et al. “Utilitarian and Deontological Ethics in Medicine.” Tropical Parasitology, Medknow Publications & Media Pvt Ltd, 2016, www.ncbi.nlm.nih.gov/pmc/articles/PMC4778182/.</ref><br />
<br />
===Free Speech and Censorship===<br />
<br />
Content moderation often finds itself in conflict with the principles of free speech, especially when the content it moderates is of a political, social or controversial nature.<ref name="Gatekeepers"></ref>. One the one hand, internet platforms are private entities with full control over what they can allow their users to post. On the other hand, large, dominant social media platforms like Facebook and Twitter have significant influence over the public discourse and act as effective monopolies on audience engagement. The ethical dilemma comes in when discussing who has the right to control what the public has to say and what gives them this right. In this sense, centralized platforms act as a modern day ''agoras'', where [[Utilitarian_Philosophy#John_Stuart_Mill|John Stuart Mill's]] "marketplace of ideas" allows good ideas to be separated from the bad without top-down influence.<ref name="Garbage"></ref> When corporations are allowed to decide with impunity what is or isn't allowed to be discussed in such a space, they circumvent this process and stifle free speech on the primary channel individuals use to express themselves.<ref name="Impossible Choices"></ref><br />
<br />
===Anonymous Social Medias===<br />
[[File:mic.png|600px|thumbnail|right|Microsoft is one of many companies who have grown to include auto-moderating bots as part of their service offerings.<ref name=microsoft></ref>]]<br />
Social media sites created with the intention of keeping users anonymous so that they may post freely is an ethical concern. [https://en.wikipedia.org/wiki/Spring.me Formspring], which is now defunct, was a platform that allowed anonymous users to ask selected individuals their questions. [https://en.wikipedia.org/wiki/Ask.fm Ask.fm]] which is a similar site, has outlived its rival, Formspring. However, a handful of content submitted by anonymous users on these sites are hateful comments that contribute to cyberbullying. There have been two suicides linked to cyberbullying on Formspring.<ref>James, Susan Donaldson. “Jamey Rodemeyer Suicide: Police Consider Criminal Bullying Charges.” ABC News, ABC News Network, 22 Sept. 2011, abcnews.go.com/.</ref><ref>“Teenager in Rail Suicide Was Sent Abusive Message on Social Networking Site.” The Telegraph, Telegraph Media Group, 22 July 2011, www.telegraph.co.uk/.</ref>. In 2013, when Formspring shut down, Ask.fm began a more active approach at content moderation. <br />
<br />
Other, similar anonymous apps include [[Yik Yak|Yik Yak]], [https://en.wikipedia.org/wiki/Secret_(app) Secret] (now defunct), and [https://en.wikipedia.org/wiki/Whisper_(app) Whisper]. Learning from their predecessors and competition, YikYak and Whisper have also taken a more active approach at Content Moderation and have not just employed people to moderate content, but also algorithms <ref>Deamicis, Carmel. “Meet the Anonymous App Police Fighting Bullies and Porn on Whisper, Yik Yak, and Potentially Secret.” Gigaom – Your Industry Partner in Emerging Technology Research, Gigaom, 8 Aug. 2014, gigaom.com/. </ref>.<br />
<br />
=== Bots ===<br />
Although a lot of content moderation cannot be dealt with using computer algorithms and must be outsourced to "virtual sweatshops", a lot of content is still moderated through the use of computer bots. The use of these computer bots naturally comes with many ethical concerns <ref>Bengani, Priyanjana. “Controlling the Conversation: The Ethics of Social Platforms and Content Moderation.” Columbia University in the City of New York, Apr. 2018, www.columbia.edu/content/.</ref>. The largest concern lies among academics, an increasing portion of whom are worried that auto-moderation cannot be effectively implemented on a global scale<ref>Newton, Casey. "Facebook’s content moderation efforts face increasing skepticism." The Verge. 24 August 2018. https://www.theverge.com/2018/8/24/17775788/facebook-content-moderation-motherboard-critics-skepticism</ref> UCLA Professor Assistant Professor Sarah Roberts said in an interview with ''Motherboard'' regarding Facebook's attempt at global auto-moderation, "it’s actually almost ridiculous when you think about it... What they’re trying to do is to resolve human nature fundamentally."<ref name=motherboard>Koebler, Jason. "The Impossible Job: Inside Facebook’s Struggle to Moderate Two Billion People." Motherboard. 23 August 2018. https://motherboard.vice.com/en_us/article/xwk9zd/how-facebook-content-moderation-works</ref> The article's objective of making clear that auto-moderation isn't feasible includes a report that Facebook CEO Mark Zuckerberg and COO Sheryl Sandberg often have to weigh in on content moderation themselves, a testament to how situational and subjective the job is.<ref name=motherboard></ref><br />
<br />
Tech companies such as Microsoft's Azure cloud service have begun offering automated content moderation packages for purchase by companies.<ref name=microsoft>"Content Moderator." Microsoft Azure. https://azure.microsoft.com/en-us/services/cognitive-services/content-moderator/</ref> The Microsoft Azure content moderator advertises expertise in image moderation, text moderation in over 100 languages that monitors for profanity and contextualized offensive language, video moderation including recognizing "racy" content, as well as a human review tool for situations where the automated moderator is unsure of what to do.<ref name=microsoft></ref><br />
<br />
==See Also==<br />
{{resource|<br />
*[[Virtual sweatshops]]<br />
*[[Bias in Information]]<br />
*[[Censorship]]<br />
*[[Facebook newsfeed curation]]<br />
*[[Censorship in China]]<br />
*[[Punishments in Virtual Environments]]<br />
*[[Reddit]]<br />
*[[Wikipedia]]<br />
*[[Yelp Reviewing]]<br />
}}<br />
<br />
==References==<br />
<br />
<references/><br />
<br />
[[Category:2019New]]<br />
[[Category:Concepts]]<br />
[[Category:Censorship]]<br />
[[Category:Media Content]]<br />
[[Category:BlueStar2019]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Defcon_(video_game)&diff=89439Defcon (video game)2020-03-17T15:40:27Z<p>WikiSysop: </p>
<hr />
<div>{{SoftwareInfoBox<br />
|NAME=Defcon (video game)<br />
|LOGO=defconlogo.jpg<br />
|SCREENIMAGE=Defconmap.jpg<br />
|IMAGEURL=http://si410wiki.sites.uofmhosting.net/images/3/3a/Defconmap.jpg<br />
|CAPTION="Defcon map"<br />
|TYPE=Video Game<br />
|TEXT=<br />
|PRODUCT=Defcon<br />
|LAUNCH=September 29, 2006<ref>https://store.steampowered.com/app/1520/DEFCON/</ref><br />
|PLATFORM=Mac OS X, Microsoft Windows, Linux<br />
|STATUS=Active<br />
|SITEURL=https://www.introversion.co.uk/introversion/<br />
|URLTEXT=https://www.introversion.co.uk/introversion/<br />
}}<br />
<br />
'''''Defcon''''' is a multiplayer nuclear war strategizing game for Windows, Mac, and Linux, developed by Introversion Software and published by Encore Software and Pinnacle Entertainment Group. The game was released on Steam on September 29, 2006 <ref>https://store.steampowered.com/app/1520/DEFCON/</ref>. Ethical computer game design, along with violence, are at the base of the ethical issues concerning Defcon.<br />
<br />
==Gameplay==<br />
In Defcon, each player is given control of a continent on Earth and given the same number of people to rule over. Along with the continent of people, come a variety of military resources they must place such as nuclear silos, radars, naval fleets, and air force bases. The game incorporates a Defcon timer that counts down from Defcon 5 to Defcon 1. Over Defcon 5 and Defcon 4, the player may place their military resources but not engage in battle. Once Defcon 3 is hit, players may engage in battle over typical military means but not in nuclear warfare. The typical military warfare continues until Defcon 1 is hit, when the players may engage in nuclear warfare. The player may also create strategic alliances with other players through the use of in-game chatting systems.<br />
<br />
Throughout the game the player aims to kill as many people as possible all while keeping to the other objective of the game; to have the greatest number of people remaining in their country among all the players. After a set amount of the total number of the nukes are deployed, Defcon 1 ends and the final battles are had within a last-minute timer. The typical game will last around 45 minutes.<br />
<br />
===Alternate Game Modes===<br />
====Office Mode====<br />
In Office Mode, Defcon runs in silent mode and behind the main desktop. The game runs in real-time and silent notifications are given whenever a key event happens in the game. This mode lasts much longer than a regular game because the regular game mode has a slower clock. The mode is designed to run in as long as a typical workday.<br />
====Speed Defcon====<br />
In Speed Mode, Defcon runs with a much faster clock and the game lasts a fraction of the time of a regular game.<br />
====Diplomacy====<br />
In Diplomacy, all players start the game as part of an alliance. The game then progresses as players began to betray the alliance in order to win the game.<br />
====BigWorld====<br />
In BigWorld mode, a bigger earth is simulated by decreasing the range and speed of a players’ military resources but at the same time increasing the number of military resources by a factor of 2. This, in turn, creates a much longer game time.<br />
<br />
<br />
== Ethical Concerns in Defcon==<br />
Defcon, much like other computer games, bring up the issue of ethical game design. Like most games, there is a free reign to choose what you want to do within the game; such as when performing ethical decisions. However, due to Defcon’s game design where killing the most amount of people possible results in winning, Defcon players lose this possibility to make an informed ethical decision. Players must launch their nukes and kill people because that is the only way they can win the game. This is only amplified by the player being able to launch nukes from above without regard for their own life because they are not located anywhere in the world. As a result, players begin to kill without thinking of the ethical concerns of nuclear fallout because they are just doing what needs to be done to win the game. Overtime, players are constrained to playing to these rules; rules that have no negative reaction to these nuclear launches. This results in, what researchers say, players of the game to believe that what they are doing is not evil <ref>https://is.muni.cz/el/1421/jaro2014/IM090/Miguel_Sicart_The_Ethics_of_Computer_Games_2009.pdf</ref>.<br />
<br />
More ethical decisions constrained by the game’s rules are decisions made within the alliance feature. Players are free to decide whether they want to make an alliance or not. Some players make want to make the ethical decision of getting in alliances in order to avoid the mutual destruction of the world. However, this again is constrained by the winning condition; where the players must kill as many people as they can while keeping their own alive. Therefore, the player is always rerouted to lying and breaking alliances in order to win <ref>https://is.muni.cz/el/1421/jaro2014/IM090/Miguel_Sicart_The_Ethics_of_Computer_Games_2009.pdf</ref>.<br />
<br />
These kinds of ethical loopholes are at the base of violent video games and what people in the industry are being critiqued about. Some examples of other violent video games that incentivize killing are [[Wikipedia:Call of Duty|Call of Duty]], [[Wikipedia:Counter-Strike: Global Offensive|Counter-Strike: Global Offensive]], and [[Wikipedia:Grand Theft Auto|Grand Theft Auto]]. People in the industry continue to advocate against this violence.<br />
<br />
<br />
== References ==<br />
<references/><br />
<br />
[[Category: Video Games]]<br />
[[Category:2020New]]<br />
[[Category:2020Object]]<br />
<br />
([[Topics|back to index]])</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Smart_Doorbell&diff=89438Smart Doorbell2020-03-17T15:38:53Z<p>WikiSysop: </p>
<hr />
<div>{{Nav-Bar|Topics##}}<br><br />
[[File:Ring doorbell.jpg|thumbnail|right|A ring doorbell installed in front of a house's front door. (source: https://www.google.com/url?sa=i&url=https%3A%2F%2Fspy-fy.com%2Fring-doorbell-invasion-of-privacy%2F&psig=AOvVaw1T_njSB066D_yXa4xssbPT&ust=1584373266748000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCMia58HonOgCFQAAAAAdAAAAABAD]]<br />
'''Smart doorbells''' are doorbells that connect to a homeowner’s smartphone via an internet connection and notifies them when someone rings their doorbell. As soon as a visitor presses the button on the doorbell, in addition to the normal doorbell noise made in the house, the owner receives a notification on their smartphone. It can also be activated via motion sensors that can sense when a person is close to the doorbell, such as on a person’s front porch. Once the doorbell is activated, a person can see and communicate with the visitor remotely through their smartphone app. <br />
<br />
==History and Growth==<br />
===The Beginning===<br />
One of the most popular and earliest smart doorbells was created in 2013, the DoorBot. Pitched on SharkTank<sup>1</sup> in November 2013, the Sharks actually passed up on the opportunity to invest because they couldn’t see how it would progress and expand to be in every house in America like the founder Jamie Siminoff claimed. Rebranded in 2014<sup>2</sup> as the Ring Doorbell, Siminoff sold his company to Amazon for over $1 billion in 2018. <br />
<br />
Today, there are many different brands of smart doorbells, however Ring still tops the industry<sup>3</sup> in popularity and sales. Other brands of smart doorbells include Nest, Skybell, Arlo, and Vivint.<sup>3</sup><br />
<br />
===Wilshire Park===<br />
In 2015, Ring doorbell offered to give 500 homeowners in Wilshire Park their product for free. After the installations, police in the area claimed that home burglaries dropped “astronomically”<sup>4</sup>. Positive media coverage on smart doorbells exploded, praising the doorbell on how it gives homeowners a sense of security. <br />
<br />
===Sense of Safety===<br />
Many people switched to smart doorbells because it gives them a sense of security. This is partly due to the positive news coverage of smart doorbells after the Wilshire park project and others like it. Positive press came from people all over the country as smart doorbells stopped home invasions and package theft in their tracks. Videos of homeowners scaring away potential thieves by speaking to them through their doorbells went viral, and the industry used it to their advantage. Ring even has a page of these viral videos on their website<sup>5</sup>, using it in their marketing efforts as to why everyone should own a smart doorbell.<br />
<br />
==Ethical concerns==<br />
<br />
===Spying===<br />
When agreeing to any of the smart doorbell’s terms and conditions upon purchasing the product, users agree, for the most part unknowingly, that employees have access to the cached videos<sup>6</sup> from the camera. Obviously, because people do not read terms and conditions in their entirety, many argue that users do not know this and that Ring misleads them. In fact, in January 2020 a Ring employee was fired for “improperly accessing Ring users' video data”.<sup>7</sup>With employees having access to videos on private property, they are likely seeing homeowner’s children playing in the front yard, watching someone change a tire in the driveway, or talking to the neighbor across the street. In a product that claims to increase your home’s security, buyers would not be thrilled to know employees have access to their camera’s footage at all times. <br />
<br />
===Without Consent===<br />
Their terms and conditions also state, for legal issues, that a user’s camera cannot point at public streets or neighbors yards<sup>6</sup>. Therefore, if someone’s camera were to point at a neighbor’s lawn, they are illegally recording their neighbor on private property. Most smart doorbell’s terms and conditions state that the person who installed the camera is at fault if legal action is taken<sup>6</sup>. However, the regular, untrained person who threw out the terms and conditions without so much as a glance at them would not know to install the camera to only include their private property. Consequently, for this to be ethical, this information should be in the directions as a person installs their smart doorbell. Simply putting crucial information in the terms and conditions is not enough these days, as it is well known that people do not read them. <br />
<br />
===Hacking===<br />
In addition, cameras connected to the internet are very susceptible to hackers. In December 2019, Ring notified more than 3000 owners asking them to change their passwords and turn on two-factor authentication after exposing their login information online<sup>8</sup>. All someone needs to gain access to the video from smart doorbells is the login information to a person’s smartphone app. From there, they can see the owner’s private property and control the app. Break-ins would be much easier once a hacker has access to their security system.<br />
<br />
A mixup within the doorbell company can also create an unintentional hacking situation. In 2016, Ring mixed up two databases which allowed users to “hack” someone else’s camera. Many people reported seeing video feed from someone else’s property, and that they couldn’t see their own property. Seeing live feed from someone else’s property is essentially stalking, and very concerning for a security company to make such a large mistake. <br />
<br />
==References==<br />
# https://qz.com/1217898/watch-shark-tank-judges-reject-jamie-siminoffs-idea-for-ring-that-amazon-just-spent-1-billion-on/<br />
# https://news.cision.com/ring/r/doorbot-announces-company-rebranding-and-new--enhanced-wi-fi-enabled-video-doorbell,c9651452<br />
# https://www.safehome.org/doorbell-cameras/best/<br />
# https://www.vice.com/en_us/article/zmjp53/how-ring-went-from-shark-tank-reject-to-americas-scariest-surveillance-company<br />
# https://tv.ring.com/category/videos/crime-prevention<br />
# https://www.marketplace.org/2019/01/17/why-doorbell-video-cameras-are-raising-some-ethical-concerns/<br />
# https://www.vice.com/en_us/article/y3mdvk/ring-fired-employees-abusing-video-data<br />
# https://www.consumerreports.org/hacking/ring-doorbell-accounts-may-be-vulnerable-to-hackers/<br />
<br />
[[Category:2020New]]<br />
[[Category:Object]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Grindr&diff=89437Grindr2020-03-17T15:37:22Z<p>WikiSysop: </p>
<hr />
<div>== Grindr ==<br />
[[File:GLogo.jpg|400px|thumb|left|Grindr Logo]]<br />
Grindr, launched in 2009, is the largest social networking app for gay, bi, trans, and queer people.<ref>''Grindr About Page'', https://www.grindr.com/about/</ref> Grindr was the first gay dating app to launch on the iTunes App Store,<ref>"<br />
Grindr was the first big dating app for gay men. Now it’s falling out of favor. ", Jon Shadel, December 6th, 2018, https://www.washingtonpost.com/lifestyle/2018/12/06/grindr-was-first-big-dating-app-gay-men-now-its-falling-out-favor/</ref> and has four million daily active users in 200 countries. The app uses geolocation through mobile device's hardware to display people from nearest to furthest away from proximity to the user.<ref> " The best LGBTQ dating apps for Android and iOS", https://www.digitaltrends.com/mobile/best-lgbt-dating-apps/, Steven Winkelman, February 6, 2020</ref><br />
<br />
Grindr has free or subscription-based versions, which offers different capabilities and are detailed later in this article. The objective of the app is to garner relationships (sexual, romantic, friendships, or networking) based on locational distance. There are filtering options, where people can choose the app to show people within desired height ranges, weight ranges, age ranges, ethnicities, and other descriptors.<ref name ="Grindr Help">"I have a specific type. How do I search using filters?", Grindr Help Center, https://help.grindr.com/hc/en-us/articles/224271948-I-have-a-specific-type-How-do-I-search-using-filters-</ref><br />
<br />
==History and Use==<br />
Grindr was created by tech entrepreneur Joel Simkai in Los Angeles, California. <ref> " ", Israeli Founder of Grindr Talks About Growing Up Gay and Coming Out to His Family, Doron Halutz,Jun 28, 2016, https://www.haaretz.com/israel-news/culture/the-israeli-loins-behind-grindr-1.5253351 </ref> The app started as an Apple app as of March 25th, 2009. There were online dating services before Grindr, such as gaydar<ref>"Gay Dar", https://www.gaydar.net/ </ref>, but they were web based platforms and not available as mobile apps.<br />
<br />
In January 2011, Grindr won the IDate Award for Best Mobile Dating App. Later that year Grindr launched the app for Android devices. Furthermore, the updated version of the app allowed users to download a free version or premium version. The premium version of the app featured no banner ads, favorites, more visible profiles, and the capability of push notifications. The application continued to win awards the following years.<ref> Grindr Named ‘Best Mobile Dating Site’ at 2011 iDate Awards, Grindr Blog, January 24, 2011, https://web.archive.org/web/20120311215017/http:/blog.grindr.com/2011/01/grindr-named-%E2%80%98best-mobile-dating-site%E2%80%99-at-2011-idate-awards/</ref><br />
<br />
Grindr began receiving criticism aroundAugust 2013 when an updated version of the app was pushed out. This update required users to provide a valid email address, which was criticized because of the decrease in anonymity. Grindr said this was to improve portability and reduce overall spam on the application.<ref> "Overrun by spambots, gay dating app Grindr to end anonymous signups", Casey Newton, Jul 26, 2013, https://www.theverge.com/2013/7/26/4560338/hey-sexy-overrun-by-spambots-gay-hookup-app-grindr-to-end-anonymity</ref><br />
<br />
In 2016 Grindr was sold at a 60 percent stake to a Chinese gaming company Beijing Kunlun Tech. Founder and CEO, Simkhai, called the sale "a huge vote of confidence in our vision to connect gay men to even more of the world around them." <ref>"Chinese Gaming Firm Buys 60% Of Gay Dating App Grindr For $93M", Jon Russell, January 11, 2016, https://techcrunch.com/2016/01/11/chinese-gaming-firm-buys-60-of-gay-dating-app-grindr-for-93m/</ref> The remainder of the company was sold to Kunlun Tech early 2018.<ref>"Grindr planning stock market listing as Chinese owner gives approval for float, Caitlin Morrison, August 30, 2018, https://www.independent.co.uk/news/business/news/grindr-stock-market-listing-gay-dating-app-kunlun-group-ipo-china-a8514476.html</ref><br />
<br />
In 2019 the United Stats Committee on Foreign Investment (CFIUS) in the United States informed Kunlun that Chinese ownership of Grindr constitutes as a national security risk. <ref>"Exclusive: Told U.S. security at risk, Chinese firm seeks to sell Grindr dating app", Carl O'Donnell, Liana B. Baker, Echo Wang, March 27, 2019, https://www.reuters.com/article/us-grindr-m-a-exclusive-idUSKCN1R809L</ref> CFIUS' have been having concerns surrounding the safety of personal data apps handle, especially if it is in any way involving the United States military or intelligence personnel. However, neither company nor CFIUS representatives commented on the case, but Kunlun has been forced to sell its stake in Grindr by June 2020.<ref>"From ‘Kànzhe?’ to ‘Guardare?’ – Grindr’s New Owner May Be Italian", Staff, January 22, 2020, https://www.wehoville.com/2020/01/22/from-kanzhe-to-guardare-grindrs-new-owner-may-be-italian/</ref><br />
<br />
==App Layout==<br />
===Account===<br />
An email, password, and date of birth is required to use Grindr. Another option is using Google or Facebook for simple login. One then has to agree to the Terms of Service to use Grindr.<br />
[[File:GProfile.png|400px|thumb|right|Grindr Profile]]<br />
<br />
===Profiles===<br />
Profile pictures are the first thing other Grindr users will see. One can choose to upload a photo or leave it blank. One can input a Display Name for the Grindr profile, which is shown. There are options to “show age,” which can be toggled to be turned off. This is followed by the “Looking For” section which depicts what type of relationship a person is looking for on the app. These include “Chat, Dates, Friends, Networking”, etc. There is an “About Me” field, which allows one to give a short description about themselves. “My Tribes” consists of words that describe the persons identity, such as jock, bear, trans, etc. The last option is to add a person’s social media account to their profile, such as Instagram, Twitter, or Facebook. <br />
<br />
===Using the App===<br />
The apps’ main page is laid out in a grid, with generally the user in the first position, and from left to right, locational distance of other users around you. There are differing features between the free version of the app and the premium version.<br />
=====Free Version=====<br />
The free version of Grindr allows creation of profile and some basic features. Free version features include:<ref> Dating Sites Reviews, March 12, 2020, https://www.datingsitesreviews.com/staticpages/index.php?page=grindr</ref><br />
* Create a Profile<br />
* Email, Password, Date of birth, Turn on location services<br />
* Edit Profile<br />
* Display Name, About Me<br />
* Stats: Age, Height, Weight, Ethnicity, Body Type, Position, Tribes, Relationship Status, Looking For<br />
* Sexual Health: HIV Status, Last Tested Date<br />
* Social: You can add your Instagram, Twitter, and Facebook account<br />
* Upload Photo<br />
* Take a photo or choose from one of your social galleries<br />
* Photo review typically takes 20-45 minutes depending on volume<br />
* Message other members and read their messages they send you<br />
* Basic filtering of matches by using their general location<br />
<br />
Some of the premium content not included:<br />
* No banner ads<br />
* See 6x the profiles, up to 600 at once<br />
* Unlimited blocks and favorites<br />
* Access to all filters and views<br />
* Chat easily with saved phrases<br />
* Send multiple photos at once<br />
=====Premium Version=====<br />
There are two types of premiums on Grindr. Grindr Xtra ($24.99) and Grindr Unlimited ($49.00) a month.<ref> "Now You Can Pay $50 Per Month for Grindr", Mikelle Street, July 25, 2019, https://www.out.com/tech/2019/7/25/now-you-can-pay-50-month-grindr</ref><br />
Premium versions' additional features:<ref>"Grindr Help", https://help.grindr.com/hc/en-us/articles/115008879108-What-is-Grindr-XTRA-</ref><br />
* No banner ads, no interstitial ads<br />
* Personalized push notifications<br />
* View up to 600 people<br />
* Chat, Tap and Favorite Users via Explore mode<br />
* Online-only view (filtering option)<br />
* Additional Filters<br />
* More Grindr Tribes<br />
* Unlimited Blocks & Favorites<br />
* Swipe through profiles<br />
* Save and send chat phrases<br />
* Quick-send recent photos<br />
* Read Receipts<br />
* Filter and mark profiles as "recently chatted"<br />
* Discreet App Icon (incognito browsing)<br />
* PIN (added security)<br />
<br />
==Gay Rights==<br />
<br />
==Sex==<br />
Grindr has been viewed as changing sex culture among gay men. Before access to locational applications and web platforms, having sex with other men always depended on places hidden from authorities. Before mobile applications there were standard cruising<ref>"Cruising for Sex", https://en.wikipedia.org/wiki/Cruising_for_sex</ref> practices (finding gay men to have sex with), including the use of personal ads in the back of magazines, phone sex operations, bars, etc. Grindr has changed this behavior, by opening the app, seeing men that are close, and having the opportunity to ask for sex, without having to leave their house. There is the possibility of immediate, on demand sex.<ref>"Grindr has changed sex culture among gay men", Eskild Heinemeier, October 13, 2017, https://sciencenordic.com/culture-denmark-sex/grindr-has-changed-sex-culture-among-gay-men/1449993</ref><ref>PHD thesis on Grindr affecting intimacy, Moller ,p.7-8, 70-80, https://www.sdu.dk/-/media/files/forskning/phd/phd_hum/afhandlinger/2017/05012017+afhandling.pdf?la=da</ref><br />
<br />
==Drug use==<br />
[[File:Gdrugs.png|400px|thumb|right|Grindr Drug Example]][[File:Emojies.jpg|400px|thumb|right|Grindr Drug Example]]<br />
Sexual minorities, such as LGBTQ, have higher rates of substance abuse than heterosexuals. Studies have shown that LGBT people are twice as likely as heterosexual adults to have used illicit drugs within a year. It is also shown that sexual minorities are more likely to experience a substance use disorder and generally a more severe substance use disorder than heterosexual counterparts. Drug use is believed to be increased in the LGBTQ community because of social stigma, discrimination, and other stressors surrounding a minority sexuality. <ref>"Substance Use and SUDs in LGBTQ* Populations", National Institute on Drug Abuse, September 2017, https://www.drugabuse.gov/related-topics/substance-use-suds-in-lgbtq-populations</ref><br />
<br />
Grindr has been criticized for rapid drug use and not doing anything about monitoring drug use. If users are looking for specific codes, they can easily find drugs on Grindr. Within Grindr profiles, the random capitalization of the let "T" refers to illicit drug use. A common screen name on the app"parTy and play", where the capitalized "T" refers to the illegal drug meth's street name, "Tina" and "play" refers to sexual encounters while on drugs. Although some users are explicit in where they stand with drugs instead of putting it into code. There are also various emojis that are used to refer to the selling or using of drugs. Grindr has tried to combat the selling of drugs, but it does not seem like people are permanently banned, just given warnings to stop. Drug dealers reference only using Grindr to sell drugs because of the lack of real censorship or action taken when their account is flagged. Under U.S. law apps are not required to take action concerning moderating drug content. However, undercover police have used the app to catch people selling drugs.<ref> "Sex and drugs: Popular gay dating app allows users to find more than a date", Denio Lourenco, August 1, 2018, https://www.nbcnews.com/feature/nbc-out/sex-drugs-popular-gay-dating-app-allows-users-find-more-n896081</ref><br />
<br />
<br />
==Underage Users==<br />
Grindr's community guidelines are adamant that users under the age of 18 are not allowed on Grindr. There is an in-app tool to report any underage user who may be improperly using the app.<ref>Grindr Community Guidelines, https://help.grindr.com/hc/en-us/articles/360009548913-Community-Guidelines</ref> During account setup, if the user is below the age of 18 it will not allow them to create a profile, but many underage users circumvent this process by lying about their age. If an account is flagged by a user for being underage it is taken seriously and the person is banned from the app.<br />
<br />
There are many issues with underage users using the app. First, a youthful person may lie about their age to older people, wanting to have sex, and then if sent photos or have sexual relationships with the underage person, with or without prior knowledge of their age, and by doing this may have broken committed felonies. Secondly, these young people could be putting themself in danger, as the application has no screening process for sexual offenders. There have been an uptick of rape cases with young men who used the app and didn't intend to have sex, but were then raped. Grindr has been criticized for their weak age verification practices and are now under scrutiny by the U.S. Government. <ref> "Tinder, Bumble, and Grindr Are Under Investigation For Allowing Minors", Victoria Song, January 31, 2020, https://gizmodo.com/tinder-bumble-and-grindr-are-under-investigation-for-1841383474</ref><br />
<br />
==Ethical Implications==<br />
This article has reviewed some inherent ethical implications from drug use to underage users. Ethical implications surrounding this app from foreign government's ownership and access to worldly homosexual locational data and private messages, to racism and body shaming. Furthermore, technology ethics comes into play when people constructing their online identity.<br />
<br />
<br />
<br />
====Chinese Ownership====<br />
China is not extraordinarily progressive when it comes to LGBTQ rights. Same sex activity is legal, and as of 2001 homosexuality was declassified as a mental illness. However, homosexual couples do not have the same legal protections available to their heterosexual counterparts and are unable to marry or adopt. t<ref>LGBT rights in China, https://en.wikipedia.org/wiki/LGBT_rights_in_China</ref> As stated before, the CFUIS has declared the selling of Grindr from Chinese company Kunlan Tech mandatory by June 2020 due to considering the Chinese having Grindr data a national security issue. Chinese government has access to people's email address, personal messages, and photos. This includes closeted powerful individuals across the world. Take Apple's CEO Tim Cook, an openly gay man, but if he has said anything explicit or done anything explicit the Chinese government could blackmail one of the world's most powerful men.<ref>"China has access to Grindr activity. We should all be worried.", Isaac Stone Fish, April 9, 2019, https://www.washingtonpost.com/opinions/2019/04/09/why-we-cant-leave-grindr-under-chinese-control/</ref><br />
Surveillance by the Chinese government, or any government within the realm of private data associates with surveillance, and who is to check the surveyors. The U.S. government was worried about "who is to check the surveyors" and deemed it necessary for another private company, that isn't associated with it's government to buy the business.<ref>"Veillance and Reciprocal Transparency: Surveillance versus Sousveillance, AR Glass, Lifeglogging, and Wearable Computing", Steve Mann</ref> <br />
<br />
Many of these statements are hypotheticals, as there has been no indication that China has done anything known yet with Grindr data, but the implications of what could be done pose ethical implications.<br />
<br />
====Discrimination====<br />
Racism and discrimination on types of gay are prevalent on Grindr. A Huff Post article provided an accurate depiction of both racism, body shaming, and discrimination on stereotypes: "Too short, or too tall (“over 5’7” and under 6’1”), Asian (“not into rice,” “gook free zone”), fat (“175lbs or less”), fem (“no broken wrists,” “masculinity is not subjective”), black (“no chocolate,” “All blacks, keep moving cuz I ain’t interested unless u can prove not all blacks are the exact same mkay?”), not as hot as the profile owner, Latino, ugly, hairy, old (“no older than 30”), closeted, uncloseted, bisexual, not bisexual, not a college guy, not a jock, a fag, into the scene, a ginger, Catholic, Republican, not “musc,” not “prof,” not “VGL.”"<ref> "'No Fats or Fems', Dale Cooper, HuffPost, October 12, 2012, https://www.huffpost.com/entry/grindr-discrimination_b_1948766</ref><br />
=====Racism=====<br />
Grindr has been criticized not only for users having blatant racial discriminating phrasing and "My Type" categories, but also in the "type" of gay men. Trying to date through apps like Grindr can lead to racist abuse and intolerance. Things are often profiles stating "no blacks" or "no latinos" that people wouldn't say in regular life. Racist comments are normalized on Grindr and common. 96 percent of users saw racist remarks in profiles in a 2015 study.<ref>"Is Sexual Racism Really Racism? Distinguishing Attitudes Toward Sexual Racism and Generic Racism Among Gay and Bisexual Men", Denton Callander, Christy E. Newman & Martin Hol, July 7, 2015, https://link.springer.com/article/10.1007/s10508-015-0487-3t </ref> **A READING CORRELATES TO TRANSPARENCY AND PEOPLE FEELING OKAY TO SAY WHATEVER. The app allows users to filter base off of race.<ref>"Why is it OK for online daters to block whole ethnic groups?", Chris Stokel-Walker, September 29, 2018, https://www.theguardian.com/technology/2018/sep/29/wltm-colour-blind-dating-app-racial-discrimination-grindr-tinder-algorithm-racism</ref><br />
=====Stereotypes / Body Shaming=====<br />
The LGBTQ community is a place where they know how it feels to be discriminated against. However, there is abundant discrimination with the common statement "No Fats No Fems" that appears frequently on the app. Another frequent statement is "Masc for Masc," which alludes to a gay man whom acts straight. These statements and group think within the app which enforce high standards of how a person should act and look.<br />
<br />
====Location and Lawful Discrimination====<br />
There are about 72 countries where being queer is illegal. Russia, Egypt, and the United Arab Emirates are all countries that have used Grindr's location data to pinpoint LGBTQ people. These countries triangulated positions to discriminate, entrap and arrest gay people. In Iran, where the app is blocked, the legal punishment can be execution.<ref>"How Grindr Changed Gay Life Forever", Even Moffitt, December 23, 2019, https://frieze.com/article/how-grindr-changed-gay-life-forever</ref><ref>"Grindr Around the World, Tom Faber, November 29, 2019, https://theface.com/society/grindr-illegal-lgbt-dating-egypt-indonesia-iran-jamaica-uganda</ref><br />
<br />
==References==<br />
{{reflist}}<br />
<br />
[[Category:2020New]]<br />
[[Category:2020Object]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Voice_imitation_algorithms&diff=89436Voice imitation algorithms2020-03-17T14:08:27Z<p>WikiSysop: </p>
<hr />
<div>'''Voice imitation algorithms''' (also known as '''[https://en.wikipedia.org/wiki/Speech_synthesis Speech synthesis]'''<ref>https://thehill.com/opinion/cybersecurity/470826-perception-wont-be-reality-once-ai-can-manipulate-what-we-see</ref>) are a form of [https://en.wikipedia.org/wiki/Synthetic_media Synthetic Media], used to imitate human speech. They achieve this by using [https://en.wikipedia.org/wiki/Machine_learning machine learning] and [https://en.wikipedia.org/wiki/Artificial_intelligence artificial intelligence] techniques<ref>https://www.sciencedirect.com/science/article/pii/S0007681319301600?via%3Dihub</ref>. The most common method of voice imitation algorithms relies on many voice samples to produce synthesized speech.<ref>https://towardsdatascience.com/you-can-now-speak-using-someone-elses-voice-with-deep-learning-8be24368fa2b</ref> <br />
<br />
== History ==<br />
===Commercial implementation===<br />
The [https://en.wikipedia.org/wiki/Speak_%26_Spell_(toy) Speak and Spell] was originally introduced in 1978 by [https://en.wikipedia.org/wiki/Texas_Instruments Texas Instruments]. It featured a keyboard and a speech synthesizer, which was used to convert words that were typed onto the keyboard into synthesized audio that it played from speakers. [[File:Screen Shot 2020-03-13 at 3.47.02 PM.png|thumbnail|Lyrebird AI]]<br />
<br />
[https://www.descript.com/lyrebird-ai?source=lyrebird Lyrebird] (also known as '''Lyrebird AI''') was a Montreal based company founded in 2017 focused on speech synthesis and voice imitation.<ref>https://www.wired.com/brandlab/2018/10/lyrebird-uses-ai-find-artificial-voice/</ref> In 2019 it was acquired by Descript, an American company focused on [https://en.wikipedia.org/wiki/Audio_editing_software audio editing software], specifically tailored towards [https://en.wikipedia.org/wiki/Podcast podcast creators].<ref>https://www.businessinsider.com/groupon-founder-andrew-mason-new-startup-descript-detour-2017-12</ref> Lyrebird AI uses artificial intelligence and voice samples to accurately replicate human speech.<br />
<br />
China-based [https://en.wikipedia.org/wiki/Technology_company technology company] [https://en.wikipedia.org/wiki/Baidu Baidu] has used [https://en.wikipedia.org/wiki/Artificial_neural_network neural networks] and [https://en.wikipedia.org/wiki/Deep_learning deep learning] to create accurate voice imitations from thousands of collected voice samples with [https://en.wikipedia.org/wiki/In-house_software in-house software] Deepvoice.<ref>https://www.technologyreview.com/f/610386/a-new-algorithm-can-mimic-your-voice-with-just-snippets-of-audio/</ref><ref>http://research.baidu.com/Blog/index-view?id=91</ref> Baidu claims that Deepvoice is capable of replicating thousands of unique voices, with less than 30 minutes of voice samples from each voice.<ref>http://research.baidu.com/Blog/index-view?id=81</ref><br />
===Research===<br />
[https://en.wikipedia.org/wiki/University_of_Delaware University of Delaware] and [https://en.wikipedia.org/wiki/Nemours_Alfred_I._duPont_Hospital_for_Children Nemours Alfred I. duPont Hospital for Children's] jointly operated Applied Science and Engineering Laboratories (also know as ASEL), has researched and developed the [https://www.asel.udel.edu/speech/ModelTalker.html Model Talker].<ref>https://www.asel.udel.edu/</ref><ref>https://www.asel.udel.edu/speech/ModelTalker.html</ref> A software which is used with [https://en.wikipedia.org/wiki/Augmentative_and_alternative_communication AAC devices] to replicate human speech to assist those with hearing or speech impairments. The ModelTalker TTS is able to convert English language text to English language synthesized speech.<br />
<br />
The [https://en.wikipedia.org/wiki/Vocoder vocoder] was invented in 1938 by [https://en.wikipedia.org/wiki/Bell_Labs Bell Labs].<ref>https://patents.google.com/patent/US2121142A/en</ref> It is a type of [https://en.wikipedia.org/wiki/Voice_codec voice codec] that analyzes and synthesizes the human voice waveforms. It is mainly used in [https://en.wikipedia.org/wiki/Data_compression#Audio audio data compression] so that voice data can be saved and utilized while using fewer bits than the original data. This allows synthesized speech algorithms to save, analyze, and output higher fidelity data to better replicate and more accurately imitate human speech.<ref>https://arxiv.org/abs/1711.10433</ref><br />
<br />
==Speech imitation in culture==<br />
===Virtual assistants===<br />
A [https://en.wikipedia.org/wiki/Virtual_assistant virtual assistant] that performs tasks for an individual based on commands or questions. The most prominent of these being [https://en.wikipedia.org/wiki/Siri Siri], a virtual assistant developed by [https://en.wikipedia.org/wiki/Apple_Inc. Apple Inc.] and utilized on Apple's various operating systems. As of 2019 Siri supports 21 different languages,<ref>https://www.globalme.net/blog/language-support-voice-assistants-compared</ref> can interpret and respond to a wide range of voice commands,<ref>https://www.cnet.com/how-to/the-complete-list-of-siri-commands/</ref> and as of 2020, can speak in 5 different english accents.<ref>https://www.lifewire.com/change-siri-to-mans-voice-4103822</ref><br />
<br />
==Ethical implications==<br />
Voice imitation algorithms have been used in [https://en.wikipedia.org/wiki/Telemarketing_fraud#Popular_scams Grandparent scams]. A type of telemarketing fraud where the scammer will call an elderly person while claiming to be a relative who has gotten themselves into some kind of trouble and needs money. This type of scam is made easier by the realistic sounding synthesized voice, which makes it harder for the person being scammed to identify the person they are speaking with as a synthesized voice.<ref>https://www.nextgov.com/emerging-tech/2019/11/ftc-explore-promises-and-potential-abuses-voice-cloning-technology/161083/</ref><br />
<br />
There have been concerns raised over the authenticity of voice recordings when one has access to realistic voice imitation software. Concerns such as if recordings of politicians in closed-door meetings can be trusted as authentic when any voice could be replicated with enough voice samples.<ref>https://www.scientificamerican.com/article/new-ai-tech-can-mimic-any-voice/</ref> Responses to the rise of synthetic media include the Deep Fake Detection Challenge (also known as DFDC), which is sponsored by several [https://en.wikipedia.org/wiki/Big_Tech big tech] companies. The DFDC incentivizes participants to help develop technology for detecting deep fakes and related synthetic media, often referred to as tampered media, with prizes for software developed by participants which helps to identify synthetic media. The goals of the DFDC are to develop detection software at a quicker pace than the AI used in the creation of tampered media.<ref>https://deepfakedetectionchallenge.ai/</ref><br />
<br />
==References==<br />
<br />
[[Category:2020New]]<br />
[[Category:2020Concept]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Vlogging&diff=89435Vlogging2020-03-17T14:07:40Z<p>WikiSysop: </p>
<hr />
<div>{{Nav-Bar|Topics#I}}<br /><br />
<br />
'''{{initial|V}}logging''', short for video blogging, is a form of media usually resembling video diaries. It is typically characterized by creators on screen speaking directly to the camera, often with their arm visibly holding the camera out in front of themselves. Some may not be edited, while others include jump cuts, sounds, text, and more. Vlogging is often discussed in the context of [[YouTube#cite_note-2|Youtube]], a video sharing platform, but can also include vlogs that are not posted there, but rather on other sites or not at all. They may be sent back and forth using various forms of media such as [[Snapchat#cite_note-2|Snapchat]] or simply via text messages.<br />
<br />
==History==<br />
=== Creation and Evolution===<br />
The term was coined by Luuk Bowman, a musician who created a blog website featuring video diaries in 2002. <ref>[https://en.wikipedia.org/wiki/Vlog/ “Vlog.”] Wikipedia, Wikimedia Foundation, 1 Jan. 2020.</ref> However, vlogs themselves have likely been around for as long as personal video cameras have. Home videos or personal videos can be considered an early form of vlogging, before the term was created and integrated into popular culture. Vlogging has since become popularized in the twenty-first century, notably through the online video-sharing platform, [[YouTube#cite_note-2|YouTube]]. YouTube launched in 2005, allowing content to be viewed and shared much more easily. One creator by the name of LonelyGirl15 who rose to prominence from 2006-2008 was one of the first to popularize the now-traditional style of vlogging where the creator speaks directly to the camera as if it were a friend. Prior content that was considered "vlogging" did not necessarily include this, which has now become the mark of a vlog. <br /><br />
<br />
Like any Internet phenomenon, vlogging began to expand as the technology required for its creation did. As small, portable cameras developed and became both more accessible and higher quality, the vlogging community saw an expansion. Similarly, the evolution of cell phone cameras especially in the 2010s helped expand vlogging into something in which many people could partake.<br />
<br />
=== Now ===<br />
By 2020, vlogging has become a huge portion of YouTube. Many YouTubers have created secondary channels just for vlogs, giving their subscribers an insight into their personality and their daily lives. Further, for some people, vlogging has become a full-time job. Many YouTube channels have gained traction through only posting vlogs as well. Daily and random chit-chat vlogging has proven to be a popular, launching channels to fame in short amounts of time. A notable example is Emma Chamberlain, an 18 year old YouTube vlogger who began creating videos during July of 2017 and had amassed 150,000 subscribers in August of the same year.<ref>[https://en.wikipedia.org/wiki/Emma_Chamberlain/ “Emma Chamberlain.”] Wikipedia, Wikimedia Foundation, 13 Mar. 2020</ref> <br /><br />
<br />
“Vlogging” has become a widely used term on the internet, popularized even more with the coining of a popular friend group of vloggers as "The Vlog Squad." The squad, with over 20 members, regularly produce content for their channels, and got their name from starring in their apparent leader David Dobrik's videos. Dobrik has published 638 vlogs with them as of March 12, 2020.<ref>Dobrik, David [https://www.youtube.com/watch?v=D-DMmIqigqo/ WE DROVE A CONVERTIBLE INTO A CAR WASH!!] YouTube, 10 Mar. 2020</ref><br />
<br />
== Popular Types of Vlogs ==<br />
Vlogs may be centered around a certain topic, giving the creator's opinion or facts on the matter. Alternatively, they may have no topic of informative purpose, and rather be a stream of consciousness, follow-me-around type of video. Some popular types of vlogs include: <ref>Sam. [https://www.vloglikepro.com/10-different-popular-types-vlogs/ “10 Different Popular Types of Vlogs.”] VlogLikePro.com, 7 Sept. 2018</ref><br />
===== Product Reviews =====<br />
These videos examine new or interesting products, often with the purpose of giving a recommendation for or against purchase. They may be sponsored advertisements or unsponsored, honest opinion pieces. <br />
===== Beauty, and Fashion =====<br />
Youtube's [[YouTube_Beauty_Community|Beauty Community]] has grown, beauty and fashion vlogs have become popular. These may include clothing hauls where creators show viewers some of their favorite new purchases, "get ready with me" makeup videos, and more.<br />
<br />
===== Day in the Life =====<br />
Another popular category which entails vloggers showing their viewers their daily routine, often with a chit-chat tone.<br />
===== Travel =====<br />
Travel vlogs have become popular as well, usually featuring artistic shots of influencers' vacations to exotic or interesting locations.<br />
<br />
==Ethical Concerns==<br />
=== Privacy ===<br />
Due to the intimate nature of vlog content, many vloggers tend to share a lot of their personal information in their videos. In "Day in My Life" videos, for example, a creator may show much of their daily routine, including their home, places they frequently visit, and their friends and families. By sharing this information online, they put their own privacy at risk. One recent development in the past few years has been viewers locating and showing up to vloggers' houses. Popular vlogger David Dobrik, whose house is frequently shown in his videos, explained that this had started happening to him and begged fans in a tweet to “PLEASE STOP COMING TO MY FUCKING HOUSE.” <ref>Skinner, Paige. [https://www.thedailybeast.com/youtube-stars-like-david-dobrik-and-colleen-ballinger-are-begging-fans-not-to-stalk-them-at-home/ “YouTube's Biggest Stars Are Begging Fans Not to Stalk Them at Home.”] The Daily Beast, The Daily Beast Company, 22 Jan. 2020</ref> Vloggers and sisters Rachel and Colleen Ballinger elaborated on the issue in their podcast, saying that it triggers fear and anxiety because their home is somewhere "where [they're] supposed to feel safe."<br />
<br />
=== Self-Image ===<br />
==== Problems for Vloggers ====<br />
Many vloggers have opened up in recent years about struggles with various mental health and disordered eating issues as a result of their vlogging and stardom. Being on camera constantly and receiving criticism from vlog viewers can cause creators feel the need to conform to societal beauty standards. The pressure for those with large followings can then lead to the development of insecurities and body image problems. One YouTube lifestyle vlogger, Meredith Foster, revealed in a video posted on August 10, 2019 that she had struggled with body dysmorphia due to the pressures of social media brought on by her large vlog following. <ref>Foster, Meredith [https://www.youtube.com/watch?v=wbTigNnD9xo/ My Eating Disorder Story | Meredith Foster] YouTube, 10 Aug. 2019</ref><br />
<br />
==== Problems for their Viewers ====<br />
Vloggers' concerns with self image can also lead to problems for the viewers. Often, vloggers promote "healthy" practices such as diets, hacks, or workout routines. This leads viewers to believe that they need to follow these tips or to alter their own behavior in order to become like their idols, the vloggers. Frequently, the viewers do not see the full story, and just what the vloggers choose to edit into the video, so this can lead to the promotion of unhealthy behavior and goals. In Meredith Foster's video regarding her eating disorder, a viewer named Maria Alejandra Mercado commented, saying "I remember telling myself 'I wanna be skinny like Meredith.'"<br />
<br />
=== Sensitive Content ===<br />
Vloggers have creative freedom, and many post all kinds of videos. As one of the major sources of vlog content, YouTube has set community guidelines<ref>[https://www.youtube.com/intl/en-GB/about/policies/#community-guidelines/ “Policies and Safety.”] YouTube, YouTube</ref> in order to ensure that inappropriate content is not posted. In the past year, seven to eight million videos have been removed every month, on average.<ref>[https://transparencyreport.google.com/youtube-policy/removals?hl=en&amp;total_removed_videos=period%3AY2019Q4%3Bexclude_automated%3Aall&amp;lu=total_removed_videos/ “YouTube Community Guidelines Enforcement.”] Google Transparency Report, Google</ref> Categories of restricted content include nudity or sexual content, harmful or dangerous content, hateful content, violent or graphic content, harassment and cyberbullying, spam, misleading metadata, and scams. Despite these procedures, content which violates the rules may go undetected. This has led to some notable scandals. <br /><br />
In January of 2018, Logan Paul posted a vlog in which he traveled through the Aokigahara forest, also known as the "Suicide Forest" due to a high number of people taking their lives there. Paul discovered a corpse, recording close-up shots of it, and posted a vlog of it. Despite violating the community guidelines, it was ultimately Paul himself who deleted the video after sharp backlash.<ref>Matsakis, Louise. [https://www.wired.com/story/logan-paul-video-youtube-reckoning/ “The Logan Paul ‘Suicide Forest’ Video Should Be a Reckoning For YouTube.”] Wired, Conde Nast, 7 Dec. 2018</ref><br />
<br />
==References==<br />
{{resource<br />
|<br />
<references /><br />
}}<br />
<br />
[[Category:2020New]]<br />
[[Category:2020Concept]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Reid_Hoffman&diff=89434Reid Hoffman2020-03-17T14:07:01Z<p>WikiSysop: </p>
<hr />
<div><br />
<h2> Summary </h2><br />
{{Infobox person<br />
| name = Reid Hoffman<br />
| image = reid_hoffman.jpg<br />
| birth_name = Reid Garrett Hoffman<br />
| birth_place = [[Palo Alto, California]], U.S.<br />
| residence = Palo Alto, California, U.S.<ref>{{cite news |first=Nicholas |last=Lemann |url=http://www.newyorker.com/magazine/2015/10/12/the-network-man |title=The Network Man |work=The New Yorker |date=October 12, 2015 |accessdate=Mar 713 2020}}</ref><ref>{{cite web |url=http://virtualglobetrotting.com/map/reid-hoffmans-house/ |title=Reid Hoffman's House in Palo Alto, California (CA), US |accessdate=May 7, 2016|date=June 16, 2012 }}</ref><br />
| alma_mater = [[Stanford University]] ([[B.S.]])<br />[[University of Oxford]] ([[M.St.]])<br />
| known_for = Paypal, Linkedin, Facebook, AirBnb<br />
| website = {{URL|http://www.reidhoffman.org/|ReidHoffman.org}}<br />
}}<br />
<p>Reid Hoffman is one of the most influential individuals in the development of modern-day connections via internet-based platforms. </p><br />
<ul><br />
<li>Reid Before Tech </li><br />
<li> Paypal </li><br />
<li> Facebook </li><br />
<li> Linkedin </li><br />
</ul><br />
<br />
<br />
<h1> Paypal </h1><br />
Before founding Linkedin, Hoffman joined an internet startup that focused on sending money electronically from person to person. This was a revolutionary idea, as credit cards were the most novel "electronic currency" at the time. However, Paypal began to grow, and Hoffman remained alongside Paypal until its eventual sale to Ebay for $1.5 billion. The group of people that were part of Paypal include some of the most impactful entrepreneurs, investors, and technologists of the 21st century, including Peter Thiel, Elon Musk, Steven Chen, and Chad Hurley. These individuals were deemed the "Paypal Mafia", as they all went on to develop major followings and influence in their respective fields of work.<br />
<br />
One of the uniting characteristics about these men was their desire to change the world. Making connections, converting to electric vehicles, individualizing video-streaming, all of these men have played an integral role in the formation of the technologically integrated environment in which society lives.<br />
<br />
<h1> Facebook </h1><br />
Coincidentally, Hoffman was approached with the opportunity to invest in a company that paralleled his vision for growing the world through mutual connectivity via an internet platform. In 2005, he had the opportunity to invest in a budding social media platform that was supposedly a hot commodity on campuses across the United States. Having a reasonable fortune from the sale of PayPal years earlier, he made a small investment in the company. Additionally, he connected the budding company with his good friend and fellow PayPal Mafia member Peter Thiel. Peter Thiel ended up being one of the largest contributors to this company's seed round of funding. Years later, the company grew and grew, netting both Hoffman and Thiel many millions of dollars. This company turned out to be Facebook. <ref>Owen Thomas, [https://www.businessinsider.com/linkedin-reid-hoffman-facebook-stake-2012-9 "LinkedIn's Reid Hoffman Made $111 Million On A $37,500 Investment In Facebook"], ''Business Insider'', Sep 10, 2012</ref><br />
<br />
<h1> Linkedin </h1><br />
[[File:Linked_In_Logo.png|200px|thumb|left|Linkedin]]<br />
<h2> Overview of the site </h2><br />
LinkedIn is first and foremost a site for managing one's professional network. A profile features a photo of the individual, a brief bio, and various information about their education history, work history, and skillsets. For many, crafting an aesthetic, informative LinkedIn profile is just as important as the experiences and skills that underlay these things.<br />
<h2> Hoffman's Impact </h2><br />
<p> Linkedin is perhaps Hoffman's greatest legacy, as he is the founder of the company. Hoffman had always wanted to create a company based around connecting individuals. In 1997, he was compelled to create a website called SocialNet. This was an internet-based platform on which people could meet for anything from tennis partners to dates. Many say that his ideas were just a few years too early, as the emergence of Facebook, online dating sites, and other connectivity platforms largely started to emerge in the mid-2000s. Hoffman had the entrepreneurial itch since day one. That, coupled with his desire to connect the world via the internet made his founding of linkedIn inevitable.<br />
<h2> How Linkedin changed the Professional World </h2><br />
Linkedin was intended to be a platform for professionals to connect and maintain a relationship via the internet. Instead of seeing a "hiring" poster or getting referrals by word of mouth, LinkedIn allowed individuals seeking jobs to be able to reach out to their connections or make public posts to see whet<br />
Hoffman's<br />
<h2> Ethical Impacts of Linkedin </h2><br />
<h3> Recruiting </h3><br />
Linkedin has changed how students and others trying to change jobs interact with the job market.<br />
<h3> Networking as a commodity </h3><br />
For some, it may seem natural that one's education is tied to one's professional life. As those who have experienced the rise of the internet are aware, college is known to be an indicator of one's knowledge and skills. Additionally, by affiliating with one's educational institution, one is able to connect with other alums or current students who might share similar ideals, experiences, etc and be in relatively closes proximity. However, while the connection between education and professional life seem natural to us, there is a major reliance upon the idea that education implies skills. In a world in which a higher education is becoming more and more expensive and online educational programs are beginning to standardize with university's education, the need for a "formal" or "traditional" education is becoming less important. Nonetheless, LinkedIn highlights the connections that people possess with their respective educational institutions and, instead of showcasing one's skills and projects, highlights the educational institution that one attended.<br />
<h3> Privacy Management </h3><br />
As many people know, Linkedin is a great way for people to get their name out there. When you Google someone, their Linkedin page is often the first page to appear, which, for many, is preferred over a personal social media profile that may reveal some unnecessary personal information about a potential job candidate. This aspect of LinkedIn for many is incredibly convenient and a great way to put your best foot forward.<br />
<br />
<h1> References </h1><br />
<br />
[[Category:2020New]]<br />
[[Category:2020Person]]</div>WikiSysophttp://si410wiki.sites.uofmhosting.net/index.php?title=Privacy_in_Venmo&diff=89433Privacy in Venmo2020-03-17T14:06:22Z<p>WikiSysop: </p>
<hr />
<div><br />
[[File:venmo.png|thumbnail|right|Venmo]]<br />
<br />
Venmo is a digital wallet that lets you make and share payments with friends. It is convenient for when you try to split a restaurant bill with your friends or pay an electric bill that you share with your roommate. However, have you ever notice the page when you enter your Venmo that shows a list of transactions of people you do not know at all? Venmo is unique compared to other similar apps in terms of how it allows people to socialize while transferring money. You can see whom your friends are sending money to and what the money is for. By searching a person’s name, you can see all of his or her transaction histories if he or she set the transactions as public. Does this public setting work as intended? How users’ information could potentially be used?<br />
<br />
<br />
<br />
== <big>'''Privacy in Venmo'''</big> ==<br />
<br />
[[File:emoji.png|thumbnail|right|The most popular emoji in Venmo transaction message]]<br />
<br />
Venmo has brought a cashless world to its customers and gives people a new way to socialize online. By default, people are sending transactions that can be viewed by everyone in the world. What does that mean? That means I know who my roommate went grocery with, whom my friend went restaurant with, even whom my friend is dating. People are unintentionally sharing information with others that they do not expect these many people to know.<br />
<br />
<br />
It was showed that from downloading Venmo public API, we can easily download users’ transactions without obtaining users’ permission if the user sets the transaction as public. This means everyone in the world, who do not even have to have the app, can make a GET request to get others’ public transaction. At the same time, most users do not understand what it means for transactions to be public in Venmo and do not consider the necessity to change the default privacy setting to private. What’s more, since Venmo requires users to write for the purpose of the transaction, most people will record what the money is for using descriptive emoji. This means anyone can easily get information about what users buy, what they do, and whom they are with. <br />
<br />
<br />
One interesting example is that people could figure out how their friend is in a relationship with another through Venmo. There was a frequent transaction in going to restaurants and buying milk tea that let the friends on Venmo get a sense that they are in a relationship. However, the person who is in the relationship is not intended for others to know right away. There are also other examples that let users reveal private information that they do not intend to share with others yet. <br />
<br />
<br />
<br />
== <big>'''Potential Risk in Venmo'''</big> ==<br />
<br />
[[File:safety.jpeg|thumbnail|right|Safety of transaction]]<br />
<br />
From what information is given to the public, cyberattacks become much easier. A grad student studying information security has shown that 115,000 transactions can be downloaded per day by a twenty-line Python script he wrote. If an attacker has a target, he or she could find a list of people whom the target always interact with and see what common activities they are doing. From this information, the attacker could craft a highly believable phishing message to scam the target.<br />
Venmo was presented as a “bank-grade security systems”. However, this claim is inaccurate. First of all, Venmo does provide some security features like security PIN, but it is optional and most users do not realize the existence of this feature. Also, Venmo does not provide that same consumer protections as banks, which means it is not FDIC-insured. <br />
<br />
<br />
<br />
== <big>'''Suggestion for Using Venmo'''</big> ==<br />
<br />
[[File:privacy.jpg|thumbnail|right|How to approach private setting]]<br />
<br />
* Try not to have a large amount of money stored in Venmo<br />
* Do not sell or purchase items through Venmo<br />
* Changing Venmo’s setting to private<br />
* Using the PIN feature on Venmo<br />
* Using alternate payment app<br />
<br />
<br />
<br />
== <big>'''Conclusion'''</big> ==<br />
While Venmo provides a great social platform, and the idea of adding socialization to the payment app is new and interesting, the policy is not well built to support relevant privacy and safety feature. Venmo still has a long way to go to make this system mature. It may be better to use a payment app only for payment purposes for no because everyone’s privacy is precious.<br />
<br />
<br />
<br />
== <big>'''References'''</big> ==<br />
https://venmo.com/<br />
<br />
https://www.wired.com/story/venmo-alternatives/<br />
<br />
https://threatpost.com/venmo-privacy-public-transactions/147830/<br />
<br />
https://techcrunch.com/2019/06/16/millions-venmo-transactions-scraped/<br />
<br />
https://www.wired.com/story/i-scraped-millions-of-venmo-payments-your-data-is-at-risk/<br />
<br />
https://www.businessinsider.com/venmo-apple-pay-cash-vs-zelle-2017-12<br />
<br />
https://www.investopedia.com/articles/personal-finance/032415/how-safe-venmo-and-why-it-free.asp<br />
<br />
https://www.praeagency.com/resources/venmo-what-is-it-and-how-can-it-benefit-your-small-business<br />
<br />
https://qz.com/359903/the-emoji-of-venmo/<br />
<br />
https://techcrunch.com/2019/06/16/millions-venmo-transactions-scraped/<br />
<br />
https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.dailydot.com%2Fdebug%2Fis-venmo-safe%2F&psig=AOvVaw1aeWgoCnAwLZJ_ruuPVN5k&ust=1584220835828000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCMCZzPmwmOgCFQAAAAAdAAAAABAD<br />
<br />
[[Category:2020New]]<br />
[[Category:2020Concept]]</div>WikiSysop