Difference between revisions of "Digital Privacy associated with advertisements"

From SI410
Jump to: navigation, search
(→‎Instagram Ads)
 
(159 intermediate revisions by the same user not shown)
Line 1: Line 1:
−
[[File:Algorithm.png|200px|thumb|right]]
 
 
{{Nav-Bar|Topics##}}<br>
 
{{Nav-Bar|Topics##}}<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>.
+
[[File:Social-Media-Privacy2-900x400.jpeg|thumb|right|600px|Social Media privacy concern <br>
 +
Copyright: https://it.wisc.edu/news/4-ways-to-better-protect-your-privacy-in-social-media-apps/]]
 +
'''Privacy''' is the information of an individual that is secret and should not be exposed to any other individuals or organizations. Now entering the technology age, '''Digital privacy''' becomes more of a concern since people engage in online activities more and more frequently. '''Digital privacy''' is often used in contexts that promote advocacy on behalf of individual and consumer privacy rights in e-services and is typically used in opposition to the business practices of many e-marketers, businesses, and companies to collect and use such information and data. Entering Internet Age, digital security and privacy become more closely related to every individual since they are easily leaked and utilized.
 +
<br>
 +
<br>
 +
'''Advertising''' is a marketing communication that sends message to promote or sell a product, service or idea to any potential customers. Sponsors of advertising are typically businesses wishing to promote their products or services. Advertising changes its form in time and have different expressions. In the Internet Age, business gradually switch their advertisement from traditional media to new media, social media advertising for example. The main internet advertisement companies include Alphabet Inc. ([[Google]], [[YouTube]]) and Meta Platforms Inc. ([[Facebook]], [[Instagram]]).
 +
<br>
 +
<br>
 +
Internet Advertising is closely associated with Digital Privacy these days. Companies use technologies like cookies to get information of the user and use that information to better sell their advertisements. Ethical issues like should companies make profit of user privacy from advertisement always arise these days. "Targeted adverts are one of the biggest moneymakers for Facebook. By utilizing the vast amount of personal data from the 1.71 billion active users, the social media giant can tailor adverts to suit your situation." <ref>https://www.privacytrust.com/blog/how-facebook-makes-money-from-personal-data.html</ref> Especially for large companies, users' private information is easy to track since they have to make google, facebook accounts in order to connect with the world on internet. Many ethical issues arise as whether it is ethical for big tech companies to make profit using their users' private information. Since user's information is highly related to the advertisement income, so the problem is inevitable.
 +
<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.
+
== Digital Privacy And Personal Data Collection ==
 +
The advertisement distribution algorithm relies heavily on personal information these days. The wide application of recommender system, a machine learning-based content delivery algorithm, recommends new content by using the knowledge of user preference. The data used to predict user preference can be user habit or even sensitive data like address and search history. Some companies even benefit from selling these personal information. The ethical concern of digital privacy is highly related to different advertisement algorithms. The concept of 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> was introduced to measure how much privacy has the platform exposed or utilized. In most of social media platforms, user information is collected unconsciously by the platform in the form of cookies, registration information, user habit, and etc. For example, most of platforms used to not have the option to turn off cookies; now even they provide the option since the law required them to do so, most people won't care and choose to accept all cookies for their convenience. <ref>https://www.amazeemetrics.com/en/blog/76-ignore-cookie-banners-the-user-behavior-after-30-days-of-gdpr/</ref> In the perspective of Information transparency, these big tech companies are definitely not transparent enough when they try to collect user information. Users usually don't know how much of their private information has been exposed to the social media platform. For example, in the Yahoo! data breaches <ref>https://www.lifelock.com/learn/data-breaches/company-data-breach</ref> , a lot of user's private information has been exposed during this attack.
 +
<br>
 +
<br>
 +
=== Data Mining ===
 +
Social Media can get to know a user's private information by data mining. Everyone can be tracked by the trail the leave on the internet. Users provide personal information that can include their name, birthdate, geographic location, and personal interests when they make an account. Additionally, companies collect data on user behaviors about how users interact on the website. For the most time, these information will be used to better serve customers or better deliver advertisements to target customers. However, companies sometimes even share users’ data with third-party entities without users’ knowledge or consent. <ref>https://sopa.tulane.edu/blog/key-social-media-privacy-issues-2020</ref>
 +
<br>
 +
<br>
  
 +
=== Privacy concerns ===
 +
In a 2014 survey, researchers found that 91% of Americans “agree” or “strongly agree” that people have lost control over how personal information is collected and used by all kinds of entities. <ref>https://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/</ref>
 +
In the survey, 80% of social media users said they were concerned about advertisers and businesses accessing the data they share on social media platforms, and 64% said the government should do more to regulate advertisers. <ref>https://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/</ref>
 +
In another survey last year, researchers found that just 9% of social media users were “very confident” that social media companies would protect their data. About half of users were not at all or not too confident their data were in safe hands. 61% of Americans in the survey have said they would like to do more to protect their privacy. Additionally, two-thirds have said current laws are not good enough in protecting people’s privacy, and 64% support more regulation of advertisers. <ref>https://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/</ref>
 +
<br>
 +
<br>
 +
<br>
  
−
== History ==
+
== Advertisement Case studies ==
−
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).
+
Advertisement are widely used by large tech companies and they have different strategies and algorithms to distribute the content. Here are some case studies of typical advertisement practices.
−
''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>
 +
=== Google Ads ===
 +
[[File:Google Ads logo.svg.png|150px|thumb|right|Source: https://ads.google.com/home/#!/]]
 +
[[Google]] LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware. Google generates most of its revenues from advertising. This includes sales of apps, purchases made in-app, digital content products on Google and YouTube, Android and licensing and service fees, including fees received for Google Cloud offerings. More than 80% of Alphabet’s revenue comes from Google ads, which generated $147 billion in revenue last year. Google has been the market leader in online advertising for well over a decade and is expected to command nearly a 29% share of digital ad spending globally in 2021. <ref>https://www.cnbc.com/2021/05/18/how-does-google-make-money-advertising-business-breakdown-.html</ref>  
 +
<br> <br>
 +
Google nearly makes money entirely from advertisement. All other free services like Gmail, Google Drive, Google Map are used to better sell advertisements. The famous saying goes "if you don't pay for the product you are the product". Google use a specific sealed second price auction algorithm to sell their ads. For example, Google determine the ranking of advertisement when a user search with a certain term by their price and relativeness to the searching term. Google collect user data through a lot of ways including Google products like Gmail and Google Map, search histories to indicate user interest and preference, and google account to track personal and geological information.
 +
<br> <br>
 +
Google places one or more cookies on each user's computer, which is used to track a person's web browsing on a large number of unrelated websites, and track their search history.  If a user is logged into a Google service, Google also uses the cookies to record which Google Account is accessing each website and doing each search. Originally the cookie did not expire until 2038, although it could be manually deleted by the user or refused by setting a browser preference.<ref name=agger>{{cite web
 +
|url=http://www.slate.com/id/2175651/
 +
|title=Google's Evil Eye: Does the Big G know too much about us?
 +
|first=Michael
 +
|last=Agger
 +
|date=October 10, 2007
 +
|access-date=October 23, 2007}}</ref> As of 2007, Google's cookie expired in two years, but renewed itself whenever a Google service is used.<ref name=agger/> As of 2011, Google said that it anonymizes the IP address data that it collects, after nine months, and the association between cookies and web accesses after 18 months.<ref name=privacyfaq>[https://www.google.com/privacy_faq.html "Privacy FAQ"], Google, accessed October 16, 2011 and December 20, 2016</ref>  As of 2016, Google's privacy policy does not promise anything about whether or when its records about the users' web browsing or searching are deleted from its records.<ref name=privacyfaq />
 +
<br> <br>
 +
Google shares this information with law enforcement and other government agencies upon receiving a request. The majority of these requests do not involve review or approval by any court or judge.<ref>[https://www.google.com/transparencyreport/userdatarequests/ "Transparency Report: User Data Requests"], Google. Retrieved December 20, 2016.</ref>
 +
<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.
+
=== YouTube Ads ===
 +
[[File:Youtube.jpeg|150px|thumb|right|Source: https://www.youtube.com/]]
 +
[[YouTube]] is an American online video sharing and social media platform owned by Google. Similar to Facebook, YouTube is a free website that earns revenue through advertising. Advertisers spend thousands of dollars to have their advertisements featured on top videos. YouTube enforces strict community and technical guidelines for all of its advertisements; the site aims to be fair and consistent with its policies in order to benefit its users, advertisers, and partners. To ensure integrity of advertisement, YouTube reserves the right to reject any advertisement from the site that is deemed inappropriate or intrusive, and refunds are not issued for promotions in which the related advertisements disabled, or suspended due to policy violations. YouTube sell their advertisement in a different way than Google as they insert advertisements at the beginning or middle of a video. Also, the censorship is more rigorous for YouTube advertisement to not include strong violence, language, sexual content, and "controversial or sensitive subjects and events, including subjects related to war, political conflicts, natural disasters and tragedies, even if graphic imagery is not shown", unless the content is "usually newsworthy or comedic and the creator's intent is to inform or entertain". <ref>Robertson, Adi (September 1, 2016). "Why is YouTube being accused of censoring vloggers?". The Verge. Vox Media. Retrieved March 25, 2017.</ref> YouTube collects user information through search records, video preference, and video interactions. Then they promote advertisements fitting user interest.
 +
<br> <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.
+
=== Facebook Ads ===
 +
[[File:Spending-a-lot-on-facebook-ads.jpeg|200px|thumb|right|Source: https://neilpatel.com/blog/spending-a-lot-on-facebook-ads/]]
 +
[[Facebook]] is a online social media owned by Meta Platforms for people to connect all over the world. Facebook has been criticized a lot for its user privacy issues, political inference by recommending users of particular preference for politicians, mass surveillance,<ref>{{Cite web|date=May 24, 2018|title=Facebook accused of conducting mass surveillance through its apps|url=http://www.theguardian.com/technology/2018/may/24/facebook-accused-of-conducting-mass-surveillance-through-its-apps|access-date=October 9, 2020|website=the Guardian|language=en}}</ref> and malicious content like fake news, conspiracy, and hate speech.<ref>{{cite news|url=https://www.theguardian.com/commentisfree/2018/dec/21/quit-facebook-privacy-scandal-private-messages|title=Is 2019 the year you should finally quit Facebook? &#124; Arwa Mahdawi|first=Arwa|last=Mahdawi|date=December 21, 2018|via=www.theguardian.com|newspaper=The Guardian}}</ref>
 +
<br> <br>
 +
Last year, Facebook generated revenues of more than $85 billion and profits of more than $29 billion from its advertisements, according to the complaint. <ref>https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case</ref> Facebook Ads, the main moneymaker of Facebook, has also received a lot of criticism for unethical use of private information along with bias and discrimination towards the target users. Cases like Minorities receiving more low-cost housing ads, and women receiving more ads for biased jobs like secretary and nursing  <ref>Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.</ref>. Facebook’s advertising exposed its deep tracking to user privacy which triggered user's concern for their privacy and data leak. <ref>https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case</ref> With only Facebook to turn to for that data, advertisers lose out on the lower prices and increased quality and innovation that come with additional competition, the agency said.
 +
<br> <br>
 +
In 2010, the ''The Wall Street Journal'' discovered that many of Facebook's high rating applications were exposing privacy information to "dozens of advertising and Internet tracking companies". The technology that those apps used was an HTTP referer that transferred the user's identity information and even user's friends' identity information to advertising companies. A blog post by a member of Facebook's team exploited that "press reports have exaggerated the implications of sharing a user ID", though he still acknowledged that the Facebook apps were exposing the user information that violates Facebook's privacy policies.<ref>{{cite web |first1=Emily |last1=Steel |first2=Geoffrey A. |last2=Fowler |title=Facebook in Privacy Breach |url=https://www.wsj.com/articles/SB10001424052702304772804575558484075236968 |website=The Wall Street Journal |date=October 18, 2010 |access-date=June 4, 2017}}</ref><ref>{{cite web |first=Dean |last=Takahashi |title=WSJ reports Facebook apps — including banned LOLapps games — transmitted private user data |url=https://venturebeat.com/2010/10/17/wsj-reports-facebook-apps-including-banned-lolapps-games-transmitted-private-user-data/ |website=[[VentureBeat]] |date=October 17, 2010 |access-date=June 4, 2017}}</ref>
 +
<br> <br>
 +
For years, users feel Facebook keep a recording of private conversations without their consent, just to better target user for customized advertisements. Users report that they have been promoted advertisements that they only mentioned in a private conversation and never searched for or liked relevant products explicitly. These thoughts and experiences lead to the belief that Facebook tracks user's private information without user consent.<ref>{{Cite web|url=https://eu.usatoday.com/story/tech/talkingtech/2019/06/27/does-facebook-listen-to-your-conversations/1478468001/|title=Is Facebook listening to me? Why those ads appear after you talk about things|date=2019-06-28|website=USA Today|language=en-US|access-date=2019-06-28}}</ref> As a response, Facebook claimed that they are not listening to private conversation. The Facebook data policy defends that Facebook only collect legal data like the information user provided, user's networks, and purchase record in order to better provide, personalize and improve their products. <ref>https://www.facebook.com/policy.php</ref>
 +
<br>
 +
<br>
  
−
=== Computation ===
+
=== Instagram Ads ===
−
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.
+
[[File:Instagram-Advertising.png|200px|thumb|right|Source: https://jemsu.com/how-to-create-ads-from-published-instagram-posts-and-stories/]]
 +
[[Instagram]] is a photo and video sharing social networking platform. In April 2012, [[Facebook]] acquired the service for approximately US$1&nbsp;billion in cash and stock. The app allows users to upload media that can be edited with filters and organized by hashtags. Posts can be shared publicly or with pre-approved followers. Users can browse other users' content by tags and locations and view trending content. Users can like photos and follow other users to add their content to a personal.<ref> https://en.wikipedia.org/wiki/Instagram </ref>
 +
<br><br>
  
−
=== Advancements In Algorithms ===
+
In October 2013, Instagram announced that advertisements would be introduced to feed users in the United States. <ref>{{cite web |first=Matthew |last=Panzarino |title=Instagram To Start Showing In-Feed Video And Image Ads To US Users |url=https://techcrunch.com/2013/10/03/instagram-starts-showing-in-feed-video-and-image-ads-to-us-users/ |website=TechCrunch |publisher=AOL |date=October 3, 2013 |access-date=April 23, 2017}}</ref><ref>{{cite web |first=Adrian |last=Covert |title=Instagram: Now with ads |url=https://money.cnn.com/2013/10/03/technology/social/instagram-ads/ |work=CNN|date=October 3, 2013 |access-date=April 23, 2017}}</ref> The first image advertisements was displayed on November 1, 2013.<ref>{{cite web |first=Chris |last=Welch |title=Instagram launches ads with sponsored post from Michael Kors |url=https://www.theverge.com/2013/11/1/5055416/instagram-launches-sponsored-posts-michael-kors-first-ad |website=The Verge |date=November 1, 2013 |access-date=April 23, 2017}}</ref><ref>{{cite web |first=Jennifer |last=Van Grove |title=The preview is over: Instagram ads are here |url=https://www.cnet.com/news/the-preview-is-over-instagram-ads-are-here/ |publisher=CNET |date=November 1, 2013 |access-date=April 23, 2017}}</ref> Video ads was introduced about a year later on October 30, 2014.<ref>{{cite web |first=Jacob |last=Kastrenakes |title=Instagram launches video ads today |url=https://www.theverge.com/2014/10/30/7131081/instagram-video-ads-launching-today |website=The Verge |date=October 30, 2014 |access-date=April 23, 2017}}</ref><ref>{{cite web |first=Paul |last=Sawers |title=Instagram video ads are rolling out today, watch 4 of them here |url=https://thenextweb.com/insider/2014/10/30/instagram-video-ads-rolling-today-watch-four/ |website=The Next Web |date=October 30, 2014 |access-date=April 23, 2017}}</ref>
−
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><br>
−
[[File:machineLearning.png|500px]]
+
  
−
The machine learning process shown above describes how machine learning algorithms can provide more features and functionality to artificial intelligence.
+
In February 2016, Instagram had 200,000&nbsp;advertisers on the application.<ref>{{cite web |first=Anthony |last=Ha |title=There Are Now 200K Advertisers on Instagram |url=https://techcrunch.com/2016/02/24/200k-advertisers-on-instagram/ |website=TechCrunch |publisher=AOL |date=February 24, 2016 |access-date=April 23, 2017}}</ref> The number of advertisement providers increased to 500,000 by September 2016,<ref>{{cite web |first=Anthony |last=Ha |title=And now there are 500K active advertisers on Instagram |url=https://techcrunch.com/2016/09/22/instagram-500k/ |website=TechCrunch |publisher=AOL |date=September 22, 2016 |access-date=April 23, 2017}}</ref> and 1&nbsp;million in March 2017.<ref>{{cite web |first=David |last=Ingram |title=Instagram says advertising base tops one million businesses |url=https://www.reuters.com/article/us-instagram-advertising-idUSKBN16T1LK |work=Reuters |date=March 22, 2017 |access-date=April 23, 2017}}</ref><ref>{{cite web |first=Ken |last=Yeung |title=Instagram now has 1 million advertisers, will launch business booking tool this year |url=https://venturebeat.com/2017/03/22/instagram-now-has-1-million-advertisers-will-launch-business-booking-tool-this-year/ |website=VentureBeat |date=March 22, 2017 |access-date=April 23, 2017}}</ref>
 +
<br>
 +
<br>
 +
<br>
  
−
== Classifications ==
+
== Ethical Dilemmas ==
−
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.
+
Now we know that how big tech companies make money from advertisements and deliver their user the ads. Ethical concerns as big tech companies better target their users from utilizing private personal information. Also, there are many ethical issues besides leak of private information. For example, companies may promote malicious and discriminating advertisements utilizing private information from user. On the other hand, big companies defends that all the information they collected are used for better improve their service. Also, they claimed that they have comprehensive policy to protect their users.
 +
<br> <br>
 +
=== Privacy and Misuse of private information ===
 +
Most of the social media and internet service require an account to perform any operations. For example, you need a google account to access resources like Google Drive and Gmail. If you are using google chrome, you may have stored your username and password in the chrome keychain. This requires the companies to be ethical towards user privacy. "When you use our services, you’re trusting us with your information. We understand this is a big responsibility and work hard to protect your information and put you in control." said in the google privacy and information page. <ref>https://policies.google.com/privacy?hl=en-US</ref>
 +
<br> <br>
 +
Some employees of these big companies complain the unethical use of information to make profit. Facebook employees say Mark Zuckerberg's obsession with growth has overridden ethical concerns and allowed hate speech and incitements to violence to spread unchecked, internal messages leaked to media outlets show. <ref>https://nypost.com/2021/10/25/facebook-employees-flag-ethical-concerns-rip-zuckerberg/</ref> In 2018, news broke that Facebook had sold information from tens of millions of users to Cambridge Analytica LLC, which used the data to profile voters and target ads toward them in the 2016 election. At the time, the FTC accused Facebook of making deceptive representations to its users about how it shares and protects their data. <ref>https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case</ref> The agency also mentioned that Facebook agreed in 2019 to pay a $5 billion penalty to settle the FTC charges that the company violated a 2012 order regarding its data privacy practices by “deceiving users about their ability to control the privacy of their personal information.”<ref>https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case</ref> “The FTC’s theory is that one would expect that if it didn’t have market power, people would shift to competitors,” said Benjamin Sirota, an antitrust lawyer with Kobre & Kim LLP who previously worked in the DOJ’s Antitrust Division. “The FTC is depicting the degradation in privacy as confirmation essentially that Facebook has market power,” he said.<ref>https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case</ref> As in the Facebook advertisement case study stated, Facebook has the probability to listen to users' private conversation and promote advertisements based on their private conversation, which is widely considered unethical by many users.  
  
−
==== Recursive Algorithms ====
 
  
−
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.
+
On the other hand, Facebook respond to ethical concerns. In an apology on Dec. 6, 2007, Zuckerberg explained his thought process behind the program, called Beacon, and announced that users would be given the option to opt out of it. “We were excited about Beacon because we believe a lot of information people want to share isn’t on Facebook, and if we found the right balance, Beacon would give people an easy and controlled way to share more of that information with their friends,” he said. "Facebook is obligated to keep the promises about privacy that it makes to its hundreds of millions of users," Jon Leibowitz, then chairman of the FTC, said at the time. "Facebook's innovation does not have to come at the expense of consumer privacy. The FTC action will ensure it will not."<ref>https://www.nbcnews.com/tech/social-media/timeline-facebook-s-privacy-issues-its-responses-n859651</ref>
  
−
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.
 
  
−
==== Serial, Parallel or Distributed ====
+
There has been a significant number of concerns towards misuse of data privacy. Google has specified what information they collect: "We collect information to provide better services to all our users — from figuring out basic stuff like which language you speak, to more complex things like which ads you’ll find most useful, the people who matter most to you online, or which YouTube videos you might like. The information Google collects, and how that information is used, depends on how you use our services and how you manage your privacy controls." <ref>https://policies.google.com/privacy?hl=en-US</ref> Also, Google gives a detailed report about what data do they use and how they use the data to better serve their users: "Cookies help to make advertising more effective. Without cookies, it’s harder for an advertiser to reach its audience, or to know how many ads were shown and how many clicks they received. Many websites, such as news sites and blogs, partner with Google to show ads to their visitors. Working with our partners, we may use cookies for a number of purposes, such as to stop you from seeing the same ad over and over again, to detect and stop click fraud, and to show ads that are likely to be more relevant (such as ads based on websites you have visited)." <ref>https://policies.google.com/technologies/ads?hl=en-US</ref>. Also, Google claims that they give user the right to disable advertisement cookies so that they won't be tracked based on cookies.
−
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.
+
  
−
==== Deterministic vs Non-Deterministic ====
 
−
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>"
 
−
Floyd, Robert W. (November 1996) ''Non-Deterministic Algorithms''. Carnegie Institute of Technology. pp. 1–17."</ref>
 
  
−
==== Exact vs Approximation ====
+
However, people show concern that they do not trust Google for their privacy claims. "The Google of 2008 is a different beast entirely. It's a company accused of privacy violations in the states and abroad. It's a company whose fast-broadening reach has given it unchecked power. And, it's a company that last month came within three hours of a Department of Justice antitrust suit." said by John Paczkowski<ref>https://consumerwatchdog.org/blog/people-dont-trust-google-anymore</ref> “When I started at Google, there was a sense that we really believed in the power of technology to make the world a better place,” LaJeunesse said. “It’s not like that any more.” <ref>theguardian.com/technology/2020/jan/03/google-executive-human-rights-activism</ref>
−
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 ====
+
=== Security ===
−
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.
+
Advertisements pose great security concerns as advertisement providers like cannot really verify all of the advertisements and whether they are harmful. Scammers can even create fake business ads on Google Ads. The consumer body said Google only required users to have a Gmail account to create adverts and that, while it did review those that were submitted, it did not verify whether the business existed or was legitimate, nor ask for proof of ID. A Google spokesperson said protecting consumers and credible businesses was its top priority. They said: “We have strict advertising policies in place to protect consumers and prohibit ads that intentionally mislead users or fail to deliver on the promoted product or service. However, Fraudsters can create and post adverts for fake businesses on Google “within hours”, according to a Which? investigation. <ref>https://www.theguardian.com/money/2020/jul/06/scammers-can-create-fake-business-ads-on-google-within-hours</ref>
  
−
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.
 
  
−
==== Divide and Conquer ====
+
It can become very dangerous as advertisement distribution are designed with algorithms to promote to very related users. As the company use private information to better target their user in order to get a higher advertisement click rate, the security concern arises since frauds can easily break through the security check and send user spam message through advertisement. Some people claim that they lose money when they are tricked by fraud advertisements.
−
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.
+
  
−
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>.
 
  
−
====Dynamic Programming====
+
On the contrary, Google claim that they have a sophisticated mechanism to protect user's interest from advertisement of malicious software. <ref>https://support.google.com/admanager/answer/1298900?hl=en</ref>. They prohibit Ads fraud by examining invalid traffic and sites. <ref>https://support.google.com/googleplay/android-developer/answer/9969955?hl=en</ref>. For the mechanism, Google have automated system to detect invalid click and they have a team dedicated to the detection of invalid activity. "The team uses several specialized tools and a wide variety of techniques based on extensive experience tracking and monitoring user behavior and analyzing scenarios. We continually upgrade our detection mechanisms to proactively combat invalid activity." said in the Google Ads policy. <ref>https://support.google.com/admanager/answer/1298900?hl=en</ref>
−
[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.
+
−
 
+
−
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].
+
−
 
+
−
==== Backtracking ====
+
−
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.
+
−
 
+
−
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]].
+
−
 
+
−
====Greedy Algorithm====
+
−
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>.
+
−
 
+
−
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.
+
−
 
+
−
== Complexity and Big-O Notation ==
+
−
[[File:Big-O.jpeg|300px|thumbnail|right|Complexity Graph]]
+
−
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.
+
−
 
+
−
== Artificial Intelligence Algorithms ==
+
−
=== Clustering ===
+
−
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>
+
−
 
+
−
==== K-Means Clustering ====
+
−
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>  
+
−
 
+
−
==== Mean-Shift Clustering ====
+
−
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>
+
−
 
+
−
==== DBSCAN Clustering ====
+
−
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'').
+
−
 
+
−
==== EM Clustering ====
+
−
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:
+
−
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.
+
−
2) The overall purpose of the algorithm is to maximize the chance or the likelihood of belonging to a cluster in the data.
+
−
 
+
−
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>
+
−
 
+
−
==== Agglomerative Hierarchical Clustering ====
+
−
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.
+
−
 
+
−
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>
+
−
 
+
−
==== Deep Learning and Neural Networks ====
+
−
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>
+
−
 
+
−
== Ethical Dilemmas ==
+
−
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. Potential ethical issues related to algorithms and computer science include issues of privacy, data gathering, and bias.
+
  
 
=== Bias ===
 
=== Bias ===
−
Given that people are the creators of algorithms, code can inherit bias from its coder or its initial source data. 
+
The unethical use of private information also results in highly biased advertisements. Since the companies gather private information to target their users, they are likely to promote advertisements that are only related to the user's private information. That may result in the users receiving biased advertisements based on their identity stereotypes.
−
 
+
<br> <br>
−
====Joy Buolamwini and Facial Recognition====
+
Companies use Artificial intelligence algorithms to deduct a user's race and promote advertisements based on their race. A 2016 investigation by ProPublica found that Facebook’s “ethnic affinities” tool could be used to exclude Black or Hispanic users from seeing specific ads. If such ads were for housing or job opportunities, the targeting could have been considered in violation of federal law. Facebook said in response it would bolster its anti-discrimination efforts. <ref>https://www.theverge.com/2021/4/9/22375366/facebook-ad-gender-bias-delivery-algorithm-discrimination</ref>  
−
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.
+
−
 
+
−
====Bias in Criminalization====
+
−
[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>
+
−
 
+
−
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>
+
−
 
+
−
==== Job Applicants ====
+
−
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.
+
−
 
+
−
=== Privacy And Data Gathering ===
+
−
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.
+
−
 
+
−
===The Filter Bubble===
+
−
[[File:Filterbubble.png|400px|thumbnail|Personalized, Online Filter Bubbles]]
+
−
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.
+
−
 
+
−
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.
+
−
====Filter Bubble in Politics====
+
−
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> 
+
−
 
+
−
[[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]]
+
−
 
+
−
===Corrupt Personalization===
+
−
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.
+
−
 
+
−
=== Agency And Accountability ===
+
−
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.
+
−
 
+
−
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.
+
−
 
+
−
====The Trolley Problem in Practice====
+
−
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.
+
−
 
+
−
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.
+
−
 
+
−
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.
+
−
 
+
−
=== Intentions and Consequences ===
+
−
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.
+
−
 
+
−
==== YouTube Radicalization ====
+
−
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.
+
  
−
==== Facebook Advertising ====
 
−
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.
 
  
−
==See also==
+
“We’ve gone above and beyond others to help prevent discrimination in ads by restricting targeting and adding transparency,” Facebook spokesman Joe Osborne said in an emailed statement. “An advertiser determined to discriminate against people can do so on any online or offline medium today, which is why laws exist…We are the only digital media platform to make such meaningful changes in ads and we’re proud of our progress.” Osborne did not respond to questions about specific ads. <ref>https://www.motherjones.com/politics/2019/12/facebook-agreed-not-to-let-its-ads-discriminate-but-they-still-can/</ref>
−
{{resource|
+
<br> <br>
−
*[[Bias in Information]]
+
−
*[[Artificial Agents]]
+
−
*[[Value Sensitive Design]]
+
−
*[[Artificial Intelligence and Technology]]
+
−
}}
+
  
 
== References ==
 
== References ==
 
<references/>
 
<references/>
−
[[Category:2019New]]
 
−
[[Category:Concepts]]
 

Latest revision as of 18:54, 11 February 2022

←Back • ↑Topics • ↑Categories

Social Media privacy concern
Copyright: https://it.wisc.edu/news/4-ways-to-better-protect-your-privacy-in-social-media-apps/

Privacy is the information of an individual that is secret and should not be exposed to any other individuals or organizations. Now entering the technology age, Digital privacy becomes more of a concern since people engage in online activities more and more frequently. Digital privacy is often used in contexts that promote advocacy on behalf of individual and consumer privacy rights in e-services and is typically used in opposition to the business practices of many e-marketers, businesses, and companies to collect and use such information and data. Entering Internet Age, digital security and privacy become more closely related to every individual since they are easily leaked and utilized.

Advertising is a marketing communication that sends message to promote or sell a product, service or idea to any potential customers. Sponsors of advertising are typically businesses wishing to promote their products or services. Advertising changes its form in time and have different expressions. In the Internet Age, business gradually switch their advertisement from traditional media to new media, social media advertising for example. The main internet advertisement companies include Alphabet Inc. (Google, YouTube) and Meta Platforms Inc. (Facebook, Instagram).

Internet Advertising is closely associated with Digital Privacy these days. Companies use technologies like cookies to get information of the user and use that information to better sell their advertisements. Ethical issues like should companies make profit of user privacy from advertisement always arise these days. "Targeted adverts are one of the biggest moneymakers for Facebook. By utilizing the vast amount of personal data from the 1.71 billion active users, the social media giant can tailor adverts to suit your situation." [1] Especially for large companies, users' private information is easy to track since they have to make google, facebook accounts in order to connect with the world on internet. Many ethical issues arise as whether it is ethical for big tech companies to make profit using their users' private information. Since user's information is highly related to the advertisement income, so the problem is inevitable.

Digital Privacy And Personal Data Collection

The advertisement distribution algorithm relies heavily on personal information these days. The wide application of recommender system, a machine learning-based content delivery algorithm, recommends new content by using the knowledge of user preference. The data used to predict user preference can be user habit or even sensitive data like address and search history. Some companies even benefit from selling these personal information. The ethical concern of digital privacy is highly related to different advertisement algorithms. The concept of Information transparency [2] was introduced to measure how much privacy has the platform exposed or utilized. In most of social media platforms, user information is collected unconsciously by the platform in the form of cookies, registration information, user habit, and etc. For example, most of platforms used to not have the option to turn off cookies; now even they provide the option since the law required them to do so, most people won't care and choose to accept all cookies for their convenience. [3] In the perspective of Information transparency, these big tech companies are definitely not transparent enough when they try to collect user information. Users usually don't know how much of their private information has been exposed to the social media platform. For example, in the Yahoo! data breaches [4] , a lot of user's private information has been exposed during this attack.

Data Mining

Social Media can get to know a user's private information by data mining. Everyone can be tracked by the trail the leave on the internet. Users provide personal information that can include their name, birthdate, geographic location, and personal interests when they make an account. Additionally, companies collect data on user behaviors about how users interact on the website. For the most time, these information will be used to better serve customers or better deliver advertisements to target customers. However, companies sometimes even share users’ data with third-party entities without users’ knowledge or consent. [5]

Privacy concerns

In a 2014 survey, researchers found that 91% of Americans “agree” or “strongly agree” that people have lost control over how personal information is collected and used by all kinds of entities. [6] In the survey, 80% of social media users said they were concerned about advertisers and businesses accessing the data they share on social media platforms, and 64% said the government should do more to regulate advertisers. [7] In another survey last year, researchers found that just 9% of social media users were “very confident” that social media companies would protect their data. About half of users were not at all or not too confident their data were in safe hands. 61% of Americans in the survey have said they would like to do more to protect their privacy. Additionally, two-thirds have said current laws are not good enough in protecting people’s privacy, and 64% support more regulation of advertisers. [8]


Advertisement are widely used by large tech companies and they have different strategies and algorithms to distribute the content. Here are some case studies of typical advertisement practices.

Source: https://ads.google.com/home/#!/

Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware. Google generates most of its revenues from advertising. This includes sales of apps, purchases made in-app, digital content products on Google and YouTube, Android and licensing and service fees, including fees received for Google Cloud offerings. More than 80% of Alphabet’s revenue comes from Google ads, which generated $147 billion in revenue last year. Google has been the market leader in online advertising for well over a decade and is expected to command nearly a 29% share of digital ad spending globally in 2021. [9]

Google nearly makes money entirely from advertisement. All other free services like Gmail, Google Drive, Google Map are used to better sell advertisements. The famous saying goes "if you don't pay for the product you are the product". Google use a specific sealed second price auction algorithm to sell their ads. For example, Google determine the ranking of advertisement when a user search with a certain term by their price and relativeness to the searching term. Google collect user data through a lot of ways including Google products like Gmail and Google Map, search histories to indicate user interest and preference, and google account to track personal and geological information.

Google places one or more cookies on each user's computer, which is used to track a person's web browsing on a large number of unrelated websites, and track their search history. If a user is logged into a Google service, Google also uses the cookies to record which Google Account is accessing each website and doing each search. Originally the cookie did not expire until 2038, although it could be manually deleted by the user or refused by setting a browser preference.[10] As of 2007, Google's cookie expired in two years, but renewed itself whenever a Google service is used.[10] As of 2011, Google said that it anonymizes the IP address data that it collects, after nine months, and the association between cookies and web accesses after 18 months.[11] As of 2016, Google's privacy policy does not promise anything about whether or when its records about the users' web browsing or searching are deleted from its records.[11]

Google shares this information with law enforcement and other government agencies upon receiving a request. The majority of these requests do not involve review or approval by any court or judge.[12]

YouTube Ads

Source: https://www.youtube.com/

YouTube is an American online video sharing and social media platform owned by Google. Similar to Facebook, YouTube is a free website that earns revenue through advertising. Advertisers spend thousands of dollars to have their advertisements featured on top videos. YouTube enforces strict community and technical guidelines for all of its advertisements; the site aims to be fair and consistent with its policies in order to benefit its users, advertisers, and partners. To ensure integrity of advertisement, YouTube reserves the right to reject any advertisement from the site that is deemed inappropriate or intrusive, and refunds are not issued for promotions in which the related advertisements disabled, or suspended due to policy violations. YouTube sell their advertisement in a different way than Google as they insert advertisements at the beginning or middle of a video. Also, the censorship is more rigorous for YouTube advertisement to not include strong violence, language, sexual content, and "controversial or sensitive subjects and events, including subjects related to war, political conflicts, natural disasters and tragedies, even if graphic imagery is not shown", unless the content is "usually newsworthy or comedic and the creator's intent is to inform or entertain". [13] YouTube collects user information through search records, video preference, and video interactions. Then they promote advertisements fitting user interest.


Facebook Ads

Source: https://neilpatel.com/blog/spending-a-lot-on-facebook-ads/

Facebook is a online social media owned by Meta Platforms for people to connect all over the world. Facebook has been criticized a lot for its user privacy issues, political inference by recommending users of particular preference for politicians, mass surveillance,[14] and malicious content like fake news, conspiracy, and hate speech.[15]

Last year, Facebook generated revenues of more than $85 billion and profits of more than $29 billion from its advertisements, according to the complaint. [16] Facebook Ads, the main moneymaker of Facebook, has also received a lot of criticism for unethical use of private information along with bias and discrimination towards the target users. Cases like Minorities receiving more low-cost housing ads, and women receiving more ads for biased jobs like secretary and nursing [17]. Facebook’s advertising exposed its deep tracking to user privacy which triggered user's concern for their privacy and data leak. [18] With only Facebook to turn to for that data, advertisers lose out on the lower prices and increased quality and innovation that come with additional competition, the agency said.

In 2010, the The Wall Street Journal discovered that many of Facebook's high rating applications were exposing privacy information to "dozens of advertising and Internet tracking companies". The technology that those apps used was an HTTP referer that transferred the user's identity information and even user's friends' identity information to advertising companies. A blog post by a member of Facebook's team exploited that "press reports have exaggerated the implications of sharing a user ID", though he still acknowledged that the Facebook apps were exposing the user information that violates Facebook's privacy policies.[19][20]

For years, users feel Facebook keep a recording of private conversations without their consent, just to better target user for customized advertisements. Users report that they have been promoted advertisements that they only mentioned in a private conversation and never searched for or liked relevant products explicitly. These thoughts and experiences lead to the belief that Facebook tracks user's private information without user consent.[21] As a response, Facebook claimed that they are not listening to private conversation. The Facebook data policy defends that Facebook only collect legal data like the information user provided, user's networks, and purchase record in order to better provide, personalize and improve their products. [22]

Instagram Ads

Source: https://jemsu.com/how-to-create-ads-from-published-instagram-posts-and-stories/

Instagram is a photo and video sharing social networking platform. In April 2012, Facebook acquired the service for approximately US$1 billion in cash and stock. The app allows users to upload media that can be edited with filters and organized by hashtags. Posts can be shared publicly or with pre-approved followers. Users can browse other users' content by tags and locations and view trending content. Users can like photos and follow other users to add their content to a personal.[23]

In October 2013, Instagram announced that advertisements would be introduced to feed users in the United States. [24][25] The first image advertisements was displayed on November 1, 2013.[26][27] Video ads was introduced about a year later on October 30, 2014.[28][29]

In February 2016, Instagram had 200,000 advertisers on the application.[30] The number of advertisement providers increased to 500,000 by September 2016,[31] and 1 million in March 2017.[32][33]


Ethical Dilemmas

Now we know that how big tech companies make money from advertisements and deliver their user the ads. Ethical concerns as big tech companies better target their users from utilizing private personal information. Also, there are many ethical issues besides leak of private information. For example, companies may promote malicious and discriminating advertisements utilizing private information from user. On the other hand, big companies defends that all the information they collected are used for better improve their service. Also, they claimed that they have comprehensive policy to protect their users.

Privacy and Misuse of private information

Most of the social media and internet service require an account to perform any operations. For example, you need a google account to access resources like Google Drive and Gmail. If you are using google chrome, you may have stored your username and password in the chrome keychain. This requires the companies to be ethical towards user privacy. "When you use our services, you’re trusting us with your information. We understand this is a big responsibility and work hard to protect your information and put you in control." said in the google privacy and information page. [34]

Some employees of these big companies complain the unethical use of information to make profit. Facebook employees say Mark Zuckerberg's obsession with growth has overridden ethical concerns and allowed hate speech and incitements to violence to spread unchecked, internal messages leaked to media outlets show. [35] In 2018, news broke that Facebook had sold information from tens of millions of users to Cambridge Analytica LLC, which used the data to profile voters and target ads toward them in the 2016 election. At the time, the FTC accused Facebook of making deceptive representations to its users about how it shares and protects their data. [36] The agency also mentioned that Facebook agreed in 2019 to pay a $5 billion penalty to settle the FTC charges that the company violated a 2012 order regarding its data privacy practices by “deceiving users about their ability to control the privacy of their personal information.”[37] “The FTC’s theory is that one would expect that if it didn’t have market power, people would shift to competitors,” said Benjamin Sirota, an antitrust lawyer with Kobre & Kim LLP who previously worked in the DOJ’s Antitrust Division. “The FTC is depicting the degradation in privacy as confirmation essentially that Facebook has market power,” he said.[38] As in the Facebook advertisement case study stated, Facebook has the probability to listen to users' private conversation and promote advertisements based on their private conversation, which is widely considered unethical by many users.


On the other hand, Facebook respond to ethical concerns. In an apology on Dec. 6, 2007, Zuckerberg explained his thought process behind the program, called Beacon, and announced that users would be given the option to opt out of it. “We were excited about Beacon because we believe a lot of information people want to share isn’t on Facebook, and if we found the right balance, Beacon would give people an easy and controlled way to share more of that information with their friends,” he said. "Facebook is obligated to keep the promises about privacy that it makes to its hundreds of millions of users," Jon Leibowitz, then chairman of the FTC, said at the time. "Facebook's innovation does not have to come at the expense of consumer privacy. The FTC action will ensure it will not."[39]


There has been a significant number of concerns towards misuse of data privacy. Google has specified what information they collect: "We collect information to provide better services to all our users — from figuring out basic stuff like which language you speak, to more complex things like which ads you’ll find most useful, the people who matter most to you online, or which YouTube videos you might like. The information Google collects, and how that information is used, depends on how you use our services and how you manage your privacy controls." [40] Also, Google gives a detailed report about what data do they use and how they use the data to better serve their users: "Cookies help to make advertising more effective. Without cookies, it’s harder for an advertiser to reach its audience, or to know how many ads were shown and how many clicks they received. Many websites, such as news sites and blogs, partner with Google to show ads to their visitors. Working with our partners, we may use cookies for a number of purposes, such as to stop you from seeing the same ad over and over again, to detect and stop click fraud, and to show ads that are likely to be more relevant (such as ads based on websites you have visited)." [41]. Also, Google claims that they give user the right to disable advertisement cookies so that they won't be tracked based on cookies.


However, people show concern that they do not trust Google for their privacy claims. "The Google of 2008 is a different beast entirely. It's a company accused of privacy violations in the states and abroad. It's a company whose fast-broadening reach has given it unchecked power. And, it's a company that last month came within three hours of a Department of Justice antitrust suit." said by John Paczkowski[42] “When I started at Google, there was a sense that we really believed in the power of technology to make the world a better place,” LaJeunesse said. “It’s not like that any more.” [43]

Security

Advertisements pose great security concerns as advertisement providers like cannot really verify all of the advertisements and whether they are harmful. Scammers can even create fake business ads on Google Ads. The consumer body said Google only required users to have a Gmail account to create adverts and that, while it did review those that were submitted, it did not verify whether the business existed or was legitimate, nor ask for proof of ID. A Google spokesperson said protecting consumers and credible businesses was its top priority. They said: “We have strict advertising policies in place to protect consumers and prohibit ads that intentionally mislead users or fail to deliver on the promoted product or service. However, Fraudsters can create and post adverts for fake businesses on Google “within hours”, according to a Which? investigation. [44]


It can become very dangerous as advertisement distribution are designed with algorithms to promote to very related users. As the company use private information to better target their user in order to get a higher advertisement click rate, the security concern arises since frauds can easily break through the security check and send user spam message through advertisement. Some people claim that they lose money when they are tricked by fraud advertisements.


On the contrary, Google claim that they have a sophisticated mechanism to protect user's interest from advertisement of malicious software. [45]. They prohibit Ads fraud by examining invalid traffic and sites. [46]. For the mechanism, Google have automated system to detect invalid click and they have a team dedicated to the detection of invalid activity. "The team uses several specialized tools and a wide variety of techniques based on extensive experience tracking and monitoring user behavior and analyzing scenarios. We continually upgrade our detection mechanisms to proactively combat invalid activity." said in the Google Ads policy. [47]

Bias

The unethical use of private information also results in highly biased advertisements. Since the companies gather private information to target their users, they are likely to promote advertisements that are only related to the user's private information. That may result in the users receiving biased advertisements based on their identity stereotypes.

Companies use Artificial intelligence algorithms to deduct a user's race and promote advertisements based on their race. A 2016 investigation by ProPublica found that Facebook’s “ethnic affinities” tool could be used to exclude Black or Hispanic users from seeing specific ads. If such ads were for housing or job opportunities, the targeting could have been considered in violation of federal law. Facebook said in response it would bolster its anti-discrimination efforts. [48]


“We’ve gone above and beyond others to help prevent discrimination in ads by restricting targeting and adding transparency,” Facebook spokesman Joe Osborne said in an emailed statement. “An advertiser determined to discriminate against people can do so on any online or offline medium today, which is why laws exist…We are the only digital media platform to make such meaningful changes in ads and we’re proud of our progress.” Osborne did not respond to questions about specific ads. [49]

References

  1. ↑ https://www.privacytrust.com/blog/how-facebook-makes-money-from-personal-data.html
  2. ↑ 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.
  3. ↑ https://www.amazeemetrics.com/en/blog/76-ignore-cookie-banners-the-user-behavior-after-30-days-of-gdpr/
  4. ↑ https://www.lifelock.com/learn/data-breaches/company-data-breach
  5. ↑ https://sopa.tulane.edu/blog/key-social-media-privacy-issues-2020
  6. ↑ https://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/
  7. ↑ https://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/
  8. ↑ https://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social-media-in-an-era-of-privacy-concerns/
  9. ↑ https://www.cnbc.com/2021/05/18/how-does-google-make-money-advertising-business-breakdown-.html
  10. ↑ 10.0 10.1 Agger, Michael · (October 10, 2007) · Google's Evil Eye: Does the Big G know too much about us? · work · 02-11-2022
  11. ↑ 11.0 11.1 "Privacy FAQ", Google, accessed October 16, 2011 and December 20, 2016
  12. ↑ "Transparency Report: User Data Requests", Google. Retrieved December 20, 2016.
  13. ↑ Robertson, Adi (September 1, 2016). "Why is YouTube being accused of censoring vloggers?". The Verge. Vox Media. Retrieved March 25, 2017.
  14. ↑ lastname, firstname · (May 24, 2018) · Facebook accused of conducting mass surveillance through its apps · work · 02-11-2022
  15. ↑ Mahdawi, Arwa · (December 21, 2018) · Is 2019 the year you should finally quit Facebook? | Arwa Mahdawi · work · 02-11-2022
  16. ↑ https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case
  17. ↑ Hao, Karen. “Facebook's Ad-Serving Algorithm Discriminate by Gender and Race.” MIT Technology Review, 5 Apr. 2019, www.technologyreview.com/.
  18. ↑ https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case
  19. ↑ lastname, firstname · (October 18, 2010) · Facebook in Privacy Breach · work · 02-11-2022
  20. ↑ Takahashi, Dean · (October 17, 2010) · WSJ reports Facebook apps — including banned LOLapps games — transmitted private user data · work · 02-11-2022
  21. ↑ lastname, firstname · (2019-06-28) · Is Facebook listening to me? Why those ads appear after you talk about things · work · 02-11-2022
  22. ↑ https://www.facebook.com/policy.php
  23. ↑ https://en.wikipedia.org/wiki/Instagram
  24. ↑ Panzarino, Matthew · (October 3, 2013) · Instagram To Start Showing In-Feed Video And Image Ads To US Users · work · AOL · 02-11-2022
  25. ↑ Covert, Adrian · (October 3, 2013) · Instagram: Now with ads · CNN · 02-11-2022
  26. ↑ Welch, Chris · (November 1, 2013) · Instagram launches ads with sponsored post from Michael Kors · work · 02-11-2022
  27. ↑ Van Grove, Jennifer · (November 1, 2013) · The preview is over: Instagram ads are here · work · CNET · 02-11-2022
  28. ↑ Kastrenakes, Jacob · (October 30, 2014) · Instagram launches video ads today · work · 02-11-2022
  29. ↑ Sawers, Paul · (October 30, 2014) · Instagram video ads are rolling out today, watch 4 of them here · work · 02-11-2022
  30. ↑ Ha, Anthony · (February 24, 2016) · There Are Now 200K Advertisers on Instagram · work · AOL · 02-11-2022
  31. ↑ Ha, Anthony · (September 22, 2016) · And now there are 500K active advertisers on Instagram · work · AOL · 02-11-2022
  32. ↑ Ingram, David · (March 22, 2017) · Instagram says advertising base tops one million businesses · Reuters · 02-11-2022
  33. ↑ Yeung, Ken · (March 22, 2017) · Instagram now has 1 million advertisers, will launch business booking tool this year · work · 02-11-2022
  34. ↑ https://policies.google.com/privacy?hl=en-US
  35. ↑ https://nypost.com/2021/10/25/facebook-employees-flag-ethical-concerns-rip-zuckerberg/
  36. ↑ https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case
  37. ↑ https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case
  38. ↑ https://news.bloomberglaw.com/antitrust/facebook-haunted-by-privacy-issues-as-ftc-boosts-antitrust-case
  39. ↑ https://www.nbcnews.com/tech/social-media/timeline-facebook-s-privacy-issues-its-responses-n859651
  40. ↑ https://policies.google.com/privacy?hl=en-US
  41. ↑ https://policies.google.com/technologies/ads?hl=en-US
  42. ↑ https://consumerwatchdog.org/blog/people-dont-trust-google-anymore
  43. ↑ theguardian.com/technology/2020/jan/03/google-executive-human-rights-activism
  44. ↑ https://www.theguardian.com/money/2020/jul/06/scammers-can-create-fake-business-ads-on-google-within-hours
  45. ↑ https://support.google.com/admanager/answer/1298900?hl=en
  46. ↑ https://support.google.com/googleplay/android-developer/answer/9969955?hl=en
  47. ↑ https://support.google.com/admanager/answer/1298900?hl=en
  48. ↑ https://www.theverge.com/2021/4/9/22375366/facebook-ad-gender-bias-delivery-algorithm-discrimination
  49. ↑ https://www.motherjones.com/politics/2019/12/facebook-agreed-not-to-let-its-ads-discriminate-but-they-still-can/
Retrieved from "http://si410wiki.sites.uofmhosting.net/index.php?title=Digital_Privacy_associated_with_advertisements&oldid=109469"