Difference between revisions of "Algorithms"
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Algorithms, are a set of precise steps along with distinct states, declared as pseudocode, used to express the detailed structure of program explaining the order of events occurring in a system.<ref>Cormen, Thomas H. et al. Introduction to Algorithms. MIT Press, 2009.</ref> Algorithms are incorporated into many aspects of daily life and are utilized in aspects of complex computer science concepts. Algorithms give people and machines the steps needed to complete tasks perfectly with more efficiency and add an element of automation to processes that are systematic and often deemed as tedious for humans. Simple processes like cooking a meal or reading a manual to assemble a new piece of furniture can be considered examples of common algorithms.<ref> “What Are Algorithms?” The Economist, The Economist Newspaper, 29 Aug. 2017, www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms. </ref>. The concept of logic is the driving force of algorithms, and these constructions are reactive towards the logical steps that embody them, which instigate ethical dilemmas which often arise from otherwise well-intended algorithms. | Algorithms, are a set of precise steps along with distinct states, declared as pseudocode, used to express the detailed structure of program explaining the order of events occurring in a system.<ref>Cormen, Thomas H. et al. Introduction to Algorithms. MIT Press, 2009.</ref> Algorithms are incorporated into many aspects of daily life and are utilized in aspects of complex computer science concepts. Algorithms give people and machines the steps needed to complete tasks perfectly with more efficiency and add an element of automation to processes that are systematic and often deemed as tedious for humans. Simple processes like cooking a meal or reading a manual to assemble a new piece of furniture can be considered examples of common algorithms.<ref> “What Are Algorithms?” The Economist, The Economist Newspaper, 29 Aug. 2017, www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms. </ref>. The concept of logic is the driving force of algorithms, and these constructions are reactive towards the logical steps that embody them, which instigate ethical dilemmas which often arise from otherwise well-intended algorithms. | ||
| − | Commonly, mathematical formulas learned throughout grade school, such as rudimentary multiplication and division methods,<ref> Algorithms for Multiplying and Dividing Whole Numbers. www.ms.uky.edu/~rwalker/ma201/3.4.pdf. </ref> are one’s first exposure to explicit algorithms, and this connection between algorithms and mathematics continues throughout educational levels, giving rise to the most complex algorithms built by man, both conceptually and ethically. As algorithms continue to grow in logical complexity through advancements in technology and human effort, embodying such concepts as artificial intelligence and machine learning<ref> McClelland, Calum. “The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning.” Medium, IoT For All, 4 Dec. 2017, medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991.</ref>, the accompanying ethical issues will grow alongside them as humans will have less and less control over these processes. | + | Commonly, mathematical formulas learned throughout grade school, such as rudimentary multiplication and division methods,<ref> Algorithms for Multiplying and Dividing Whole Numbers. www.ms.uky.edu/~rwalker/ma201/3.4.pdf. </ref> are one’s first exposure to explicit algorithms, and this connection between algorithms and mathematics continues throughout educational levels, giving rise to the most complex algorithms built by man, both conceptually and ethically. However, one of the more common algorithms that people encounter are algorithms intended to filter and personalize results based on the user. As algorithms continue to grow in logical complexity through advancements in technology and human effort, embodying such concepts as artificial intelligence and machine learning<ref> McClelland, Calum. “The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning.” Medium, IoT For All, 4 Dec. 2017, medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991.</ref>, the accompanying ethical issues will grow alongside them as humans will have less and less control over these processes. |
== History == | == History == | ||
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=== Privacy And Data Gathering === | === 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. “The Ethics of Information Transparency.” Ethics and Information Technology, vol. 11, no. 2, 2009, pp. 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. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref> Griffin, Andrew. “Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth.” The Independent, Independent Digital News and Media, 4 Oct. 2017, www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html. </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 ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency <ref> Turilli, Matteo, and Luciano Floridi. “The Ethics of Information Transparency.” Ethics and Information Technology, vol. 11, no. 2, 2009, pp. 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. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.<ref> Griffin, Andrew. “Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth.” The Independent, Independent Digital News and Media, 4 Oct. 2017, www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html. </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. | ||
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| + | ===The Filter Bubble=== | ||
| + | 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> Eli Pariser. “The Filter Bubble.” Penguin Books, 2012. </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. | ||
| + | |||
=== Intentions And Consequences === | === Intentions And Consequences === | ||
Revision as of 18:19, 26 March 2019
Contents
Algorithms
Algorithms, are a set of precise steps along with distinct states, declared as pseudocode, used to express the detailed structure of program explaining the order of events occurring in a system.[1] Algorithms are incorporated into many aspects of daily life and are utilized in aspects of complex computer science concepts. Algorithms give people and machines the steps needed to complete tasks perfectly with more efficiency and add an element of automation to processes that are systematic and often deemed as tedious for humans. Simple processes like cooking a meal or reading a manual to assemble a new piece of furniture can be considered examples of common algorithms.[2]. The concept of logic is the driving force of algorithms, and these constructions are reactive towards the logical steps that embody them, which instigate ethical dilemmas which often arise from otherwise well-intended algorithms.
Commonly, mathematical formulas learned throughout grade school, such as rudimentary multiplication and division methods,[3] are one’s first exposure to explicit algorithms, and this connection between algorithms and mathematics continues throughout educational levels, giving rise to the most complex algorithms built by man, both conceptually and ethically. However, one of the more common algorithms that people encounter are algorithms intended to filter and personalize results based on the user. As algorithms continue to grow in logical complexity through advancements in technology and human effort, embodying such concepts as artificial intelligence and machine learning[4], the accompanying ethical issues will grow alongside them as humans will have less and less control over these processes.
History
Earliest usage of algorithms can be traced back to 300 BC, present in ancient Babylonian culture used to keep track of farming and livestock. On a basic level, an algorithm is a system working through different iterations of a process, [5] 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 “algorithm” was adopted from this title.[6]
Computation
Another cornerstone for algorithms as known today relates to Alan Turing and his contributions to cognitive and 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 led to the development of Turing Machines, which capitalized on these theoretical algorithms to perform unique functions, and this led to the development of the first computers, and, subsequently, the entire field of computer science.[7] As their name suggests, computers utilized specific rules, or algorithms, to compute, and it is these machines (or sometimes people)[8] 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, calculating and controlling an immense quantity of facets of daily life.
Advancements In Algorithms
In the years following Alan Turing’s contributions, computer algorithms increased in magnitude and complexity, and advanced algorithms such as artificial intelligence utilizing machine learning capabilities were developed.[9] This level of algorithmic improvement provided the foundation for even more technological advancement and helped shape human society to the way it is today.
Ethical Dilemmas
With the relevance of algorithms as well as their sheer magnitude, ethical dilemmas were bound to arise. There is a vast list of potential ethical issues relating to algorithms and computer science, including issues of privacy, data gathering, and bias.
Bias
Just like it can be found in society, bias can also be seen in algorithms. Joy Buolamwini, a graduate computer science student at MIT, experienced an example of this. The facial recognition software she was working on was unable 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 did not recognize her face as it was supposed to.[10] 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.
Privacy And Data Gathering
The ethical issue of privacy is also highly relevant to the concept of algorithms. Information transparency [11] 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. In 2013, Yahoo was hacked, leading to the leak of data pertaining to approximately three billion users.[12] 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
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. [13] 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.
Intentions And Consequences
The ethical issues that are common in algorithm implementations can be both deliberate and unintentional. The bias in the facial recognition software that Joy Buolamwini used was an unintentional mistake that arose from a lack of machine learning data. Issues of this sort could lead to problems as facial recognition algorithms become more commonplace. It is important to consider possible faults like this when developing new algorithms, especially as algorithms become increasingly important in defining human lives. Conversely, Yahoo’s algorithm intentionally procured and stored the data of its users, and while this is not necessarily malicious in its own right, this was an issue when its information was leaked to the public. Data gathering and privacy within algorithms is an important ethical concept to be considered by developers so that other ethical fallouts are avoided in the future.
Algorithms are present throughout human society, and they control the way the world is run. There are many ethical concerns to be considered, and with more advanced algorithms in development every day, it becomes increasingly necessary for these concerns to be monitored and handled appropriately.
References
- ↑ Cormen, Thomas H. et al. Introduction to Algorithms. MIT Press, 2009.
- ↑ “What Are Algorithms?” The Economist, The Economist Newspaper, 29 Aug. 2017, www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms.
- ↑ Algorithms for Multiplying and Dividing Whole Numbers. www.ms.uky.edu/~rwalker/ma201/3.4.pdf.
- ↑ McClelland, Calum. “The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning.” Medium, IoT For All, 4 Dec. 2017, medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991.
- ↑ “A Brief History of Algorithms (and Why It's so Important in Automation, Machine Learning, and Everyday Life).” e27, e27.co/brief-history-algorithms-important-automation-machine-learning-everyday-life-20161207/.
- ↑ Das, Souvik DasSouvik. “The Origin and Evolution of Algorithms.” Digit, Digit Www.digit.in, 3 May 2016, www.digit.in/science-and-technology/the-origin-of-algorithms-30045.html.
- ↑ "Alan Turing: Algorithms, Computation, Machines.” Brooklyn Institute for Social Research, thebrooklyninstitute.com/items/courses/alan-turing-algorithms-computation-machines/.
- ↑ “Human Computers.” NASA, NASA, crgis.ndc.nasa.gov/historic/Human_Computers.
- ↑ “The History of Artificial Intelligence.” Science in the News, 28 Aug. 2017, sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/.
- ↑ Buolamwini, Joy. “How I'm Fighting Bias in Algorithms – MIT Media Lab.” MIT Media Lab, www.media.mit.edu/posts/how-i-m-fighting-bias-in-algorithms/.
- ↑ Turilli, Matteo, and Luciano Floridi. “The Ethics of Information Transparency.” Ethics and Information Technology, vol. 11, no. 2, 2009, pp. 105–112., doi:10.1007/s10676-009-9187-9.
- ↑ Griffin, Andrew. “Yahoo Admits It Accidentally Leaked the Personal Details of Half the People on Earth.” The Independent, Independent Digital News and Media, 4 Oct. 2017, www.independent.co.uk/life-style/gadgets-and-tech/news/yahoo-hack-details-personal-information-was-i-compromised-affected-leak-a7981671.html.
- ↑ Eli Pariser. “The Filter Bubble.” Penguin Books, 2012.
