Difference between revisions of "Predictive Analytics"

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Predictive analytics is the use of algorithms and machine learning techniques to forecast future events in real-time. Leveraging vast data sets, these algorithms are able to predict potential risks and costs, or even an individual’s future behaviour.<ref>Nyce, Charles. "Predictive Analytics White Paper." <i>The Digital Insurer</i>, American Institute for CPCU, 2007, www.the-digital-insurer.com/wp-content/uploads/2013/12/78-Predictive-Modeling-White-Paper.pdf.</ref> Thanks to rapid advancements in technology and the emergence of big data, predictive analytics has seen growing use in various industries, from providing health care treatment recommendations, to assessing and determining candidates for hire, and even to help police anticipate potential crimes and criminals.<ref>Cohen, I. G., et al. "The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care." <i>Health Affairs</i>, vol. 33, no. 7, 2014, pp. 1139-47, doi:10.1377/hlthaff.2014.0048.</ref><ref name="DrewHarwell">Harwell, Drew. "A Face-Scanning Algorithm Increasingly Decides Whether You Deserve The Job." <i>Washington Post</i>, 2019, www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning-algorithm-increasingly-decides-whether-you-deserve-job.</ref><ref>Perry, Walter, et al. "Predictive Policing: The Role Of Crime Forecasting In Law Enforcement Operations." RAND Corporation, 2013, doi:10.7249/rr233.</ref> As technology continues to improve, data analytics and artificial intelligence will only continue to grow their capabilities and expand their applications. However, with predictive analytics becoming increasingly prevalent in decision-making processes that have direct and potentially life-changing impacts on people’s lives, this raises serious ethical concerns regarding algorithmic bias, transparency, and data privacy.<ref name="RainerM">Mühlhoff, Rainer. "Predictive Privacy: Towards An Applied Ethics Of Data Analytics." <i>SSRN</i>, 2020, doi:10.2139/ssrn.3724185.</ref>
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Predictive analytics is the use of [https://en.wikipedia.org/wiki/Algorithm algorithms] and [https://en.wikipedia.org/wiki/Machine_learning machine learning] techniques to forecast future events in real-time. Leveraging vast data sets, these algorithms are able to predict potential risks and costs, or even an individual’s future behaviour.<ref>Nyce, Charles. "Predictive Analytics White Paper." <i>The Digital Insurer</i>, American Institute for CPCU, 2007, www.the-digital-insurer.com/wp-content/uploads/2013/12/78-Predictive-Modeling-White-Paper.pdf.</ref> Thanks to rapid advancements in technology and the emergence of big data, predictive analytics has seen growing use in various industries, from providing health care treatment recommendations to assessing and determining candidates for hire and even to help police anticipate potential crimes and criminals.<ref>Cohen, I. G., et al. "The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care." <i>Health Affairs</i>, vol. 33, no. 7, 2014, pp. 1139-47, doi:10.1377/hlthaff.2014.0048.</ref><ref name="HarwellDrew">Harwell, Drew. "Rights group files federal complaint against AI-hiring firm HireVue, citing ‘unfair and deceptive’ practices." <i>Washington Post</i>, 6 November 2019, www.washingtonpost.com/technology/2019/11/06/prominent-rights-group-files-federal-complaint-against-ai-hiring-firm-hirevue-citing-unfair-deceptive-practices.</ref><ref>Perry, Walter, et al. "Predictive Policing: The Role Of Crime Forecasting In Law Enforcement Operations." RAND Corporation, 2013, doi:10.7249/rr233.</ref> As technology continues to improve, data analytics and artificial intelligence continue to grow their capabilities and expand their applications. However, with predictive analytics becoming increasingly prevalent in decision-making processes that have direct and potentially life-changing impacts on people’s lives, ethical concerns regarding algorithmic bias, transparency, and data privacy are revealed.<ref name="RainerM">Mühlhoff, Rainer. "Predictive Privacy: Towards An Applied Ethics Of Data Analytics." <i>SSRN</i>, 2020, doi:10.2139/ssrn.3724185.</ref>
  
 
==Ethical Challenges==  
 
==Ethical Challenges==  
  
 
===Bias and Discrimination===
 
===Bias and Discrimination===
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Ever since the rise of the computer gaming industry brought back the resurgence of neural networks, experts have argued that deep learning is the most effective way to train an artificial intelligence system.<ref name="LarryHardesty">Hardesty, Larry. "Explained: Neural Networks." <i>MIT News</i>, 2021, news.mit.edu/2017/explained-neural-networks-deep-learning-0414.</ref> Designed to mimic the way a human brain thinks and makes decisions, a network of thousands or millions of individual processing nodes are connected together in a neural net, which enables an algorithm to train itself to perform a task given a prepared training data set.<ref name="LarryHardesty"></ref> However, according to Barocas and Selbst from Cornell University and UCLA respectively, “an algorithm is only as good as the data it works with."<ref name="BarocasAndrew">Barocas, Solon, and Andrew D. Selbst. "Big Data's Disparate Impact." <i>SSRN</i>, 2016, doi:10.2139/ssrn.2477899.</ref> Zarsky, a professor and vice dean at Haifa University, argues that algorithms trained on biased data sets will not only inherit pre-existing biases from the aforementioned data set but also generate novel patterns of unfair bias and discrimination and reinforce these patterns in their decision-making processes.<ref>Zarsky, Tal Z. "An Analytic Challenge: Discrimination Theory in the Age of Predictive Analytics." <i>I/S: A Journal of Law and Policy</i>, vol. 14.1, 2017, pp. 12-35, kb.osu.edu/bitstream/handle/1811/86702/1/ISJLP_V14N1_011.pdf.</ref> Some surmise an algorithm may even interpret inequalities in historical data as sensible patterns, which in turn supports existing societal biases; furthermore, detecting and addressing unfair bias and discrimination in algorithms for predictive analytics is particularly difficult as these frequently occur because of unintended consequences from using the algorithm and not the purposeful actions of an ill-intentioned programmer.<ref name="BarocasAndrew"></ref>
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Ever since the rise of the computer gaming industry brought back the resurgence of neural networks, experts have argued that deep learning is a highly effective way to train an artificial intelligence system.<ref name="LarryHardesty">Hardesty, Larry. "Explained: Neural Networks." <i>MIT News</i>, 2021, news.mit.edu/2017/explained-neural-networks-deep-learning-0414.</ref> Designed to mimic the way a human brain thinks and makes decisions, a network of thousands or millions of individual processing nodes are connected together in a neural net, which enables an algorithm to train itself to perform a task given a prepared training data set.<ref name="LarryHardesty"></ref> However, according to Barocas and Selbst from Cornell University and UCLA respectively, “an algorithm is only as good as the data it works with."<ref name="BarocasAndrew">Barocas, Solon, and Andrew D. Selbst. "Big Data's Disparate Impact." <i>SSRN</i>, 2016, doi:10.2139/ssrn.2477899.</ref> Zarsky, a professor and vice dean at Haifa University, argues that algorithms trained on biased data sets will not only inherit pre-existing biases from the aforementioned data set but also generate novel patterns of unfair bias and discrimination and reinforce these patterns in their decision-making processes.<ref>Zarsky, Tal Z. "An Analytic Challenge: Discrimination Theory in the Age of Predictive Analytics." <i>I/S: A Journal of Law and Policy</i>, vol. 14.1, 2017, pp. 12-35, kb.osu.edu/bitstream/handle/1811/86702/1/ISJLP_V14N1_011.pdf.</ref> Some surmise an algorithm may even interpret inequalities in historical data as sensible patterns, which in turn supports existing societal biases; furthermore, detecting and addressing unfair bias and discrimination in algorithms for predictive analytics is particularly difficult as these frequently occur because of unintended consequences from using the algorithm and not the purposeful actions of an ill-intentioned programmer.<ref name="BarocasAndrew"></ref>
  
 
===Transparency===
 
===Transparency===
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Some assert that transparency as an ethical issue is in opposition of other ethical interests such as privacy.<ref name="CansuCanca">Canca, Cansu. "Anonymity in the Time of a Pandemic: Privacy vs. Transparency." <i>Bill of Health</i>, Harvard Law, blog.petrieflom.law.harvard.edu/2020/03/30/anonymity-in-the-time-of-a-pandemic-privacy-vs-transparency. Accessed 27 March 2021.</ref> Transparency in algorithms requires both accessibility and comprehensibility of information about the algorithm, as an algorithm requires accessibility to its information in order to be effective.<ref name="BrentDaniel">Mittelstadt, Brent D., et al. "The Ethics Of Algorithms: Mapping The Debate." <i>Big Data & Society</i>, vol. 3, no. 2, 2016, pp. 1-21, SAGE Publications, doi:10.1177/2053951716679679.</ref> Many modern sophisticated artificial intelligence systems are trained via deep learning comprised of extensive neural networks that can reach up to fifty layers.<ref name="LarryHardesty"></ref> As each layer adds complexity, Sloan and Warner assert that the human comprehensibility of these networks is affected.<ref>Sloan, Robert H., Richard Warner. "When Is an Algorithm Transparent?: Predictive Analytics, Privacy, and Public Policy." <i>IEEE: Security & Privacy</i>, SSRN, 2017, dx.doi.org/10.2139/ssrn.3051588.</ref>
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Some assert that transparency as an ethical issue is in opposition to other ethical interests such as privacy.<ref name="CansuCanca">Canca, Cansu. "Anonymity in the Time of a Pandemic: Privacy vs. Transparency." <i>Bill of Health</i>, Harvard Law, blog.petrieflom.law.harvard.edu/2020/03/30/anonymity-in-the-time-of-a-pandemic-privacy-vs-transparency. Accessed 27 March 2021.</ref> Transparency in algorithms means that the algorithm should not only have its details accessible but also comprehensible to humans analyzing them; accessible information that is not decipherable is no longer useful.<ref name="BrentDaniel">Mittelstadt, Brent D., et al. "The Ethics Of Algorithms: Mapping The Debate." <i>Big Data & Society</i>, vol. 3, no. 2, 2016, pp. 1-21, SAGE Publications, doi:10.1177/2053951716679679.</ref> Many modern sophisticated artificial intelligence systems are trained via deep learning that is comprised of extensive neural networks reaching up to fifty layers.<ref name="LarryHardesty"></ref> As each layer adds complexity, Sloan and Warner assert that the human comprehensibility of these networks is affected and thus the transparency.<ref>Sloan, Robert H., Richard Warner. "When Is an Algorithm Transparent?: Predictive Analytics, Privacy, and Public Policy." <i>IEEE: Security & Privacy</i>, SSRN, 2017, dx.doi.org/10.2139/ssrn.3051588.</ref>
  
 
===Predictive Privacy===
 
===Predictive Privacy===
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The term “predictive privacy” refers to the ethical challenges facing both privacy and data protection that are posed by the ability of algorithms to predict sensitive information about an individual using a large data set of other individuals <ref name="RainerM">Mühlhoff, Rainer. "Predictive Privacy: Towards An Applied Ethics Of Data Analytics". SSRN Electronic Journal, 2020. Elsevier BV, doi:10.2139/ssrn.3724185.</ref>. In 2019, the Electronic Privacy Information Center (EPIC) raised this very ethical concern in their official complaint to the Federal Trade Commission against HireVue, a recruiting-technology company, in which they said that “the company’s use of unproven artificial-intelligence systems that scan people’s faces and voices constituted a wide-scale threat to American workers. <ref name="DrewHarwell">Harwell, Drew. "A Face-Scanning Algorithm Increasingly Decides Whether You Deserve The Job". Washington Post, 2019, https://www.washingtonpost.com/technology/2019/10/22/ai-hiring-face-scanning-algorithm-increasingly-decides-whether-you-deserve-job/. </ref>”. Mühlhoff’s definition of a violation of predictive privacy is that “if sensitive information about that person or group is predicted against their will or without their knowledge on the basis of data of many other individuals, provided that these predictions lead to decisions that affect anyone’s social, economic, psychological, physical, … well-being or freedom. <ref name="RainerM">Mühlhoff, Rainer. "Predictive Privacy: Towards An Applied Ethics Of Data Analytics". SSRN Electronic Journal, 2020. Elsevier BV, doi:10.2139/ssrn.3724185.</ref>” Most importantly, predictive privacy can still be violated regardless of the prediction’s accuracy. Any information predicted against one’s will that leads to life-affecting decisions could be considered a violation of predictive privacy, but when systems for data collection and processing are designed such that subjects cannot provide meaningful or informed consent <ref>Schermer, Bart W. "The Limits Of Privacy In Automated Profiling And Data Mining". Computer Law & Security Review, vol 27, no. 1, 2011, pp. 45-52. Elsevier BV, doi:10.1016/j.clsr.2010.11.009. </ref>, then perhaps predictive privacy needs to be raised more seriously, especially when people’s lives are potentially at stake.
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The term “predictive privacy” refers to the ethical challenges posed by the ability of algorithms to predict sensitive information about an individual using information derived from data sets of other individuals.<ref name="RainerM"></ref> In 2019, the [https://en.wikipedia.org/wiki/Electronic_Privacy_Information_Center Electronic Privacy Information Center (EPIC)] raised this ethical concern in their official complaint to the [https://en.wikipedia.org/wiki/Federal_Trade_Commission Federal Trade Commission (FTC)] against HireVue, a recruiting-technology company, stating that “the company’s use of unproven artificial-intelligence systems that scan people’s faces and voices [constitutes] a wide-scale threat to American workers."<ref name="HarwellDrew"></ref> Mühlhoff’s definition of a violation of predictive privacy is “if sensitive information about [a] person or group is predicted against their will or without their knowledge on the basis of data of many other individuals, provided that these predictions lead to decisions that affect anyone’s...freedom."<ref name="RainerM"></ref> Some note that predictive privacy can be violated regardless of the prediction’s accuracy, especially when systems for data collection and processing are designed such that subjects cannot provide meaningful or informed consent.<ref>Schermer, Bart W. "The Limits Of Privacy In Automated Profiling And Data Mining." <i>Computer Law & Security Review</i>, vol. 27, no. 1, 2011, pp. 45-52, doi:10.1016/j.clsr.2010.11.009.</ref>
  
 
==References==
 
==References==
 
<references/>
 
<references/>

Revision as of 20:07, 28 March 2021

Predictive analytics is the use of algorithms and machine learning techniques to forecast future events in real-time. Leveraging vast data sets, these algorithms are able to predict potential risks and costs, or even an individual’s future behaviour.[1] Thanks to rapid advancements in technology and the emergence of big data, predictive analytics has seen growing use in various industries, from providing health care treatment recommendations to assessing and determining candidates for hire and even to help police anticipate potential crimes and criminals.[2][3][4] As technology continues to improve, data analytics and artificial intelligence continue to grow their capabilities and expand their applications. However, with predictive analytics becoming increasingly prevalent in decision-making processes that have direct and potentially life-changing impacts on people’s lives, ethical concerns regarding algorithmic bias, transparency, and data privacy are revealed.[5]

Ethical Challenges

Bias and Discrimination

Ever since the rise of the computer gaming industry brought back the resurgence of neural networks, experts have argued that deep learning is a highly effective way to train an artificial intelligence system.[6] Designed to mimic the way a human brain thinks and makes decisions, a network of thousands or millions of individual processing nodes are connected together in a neural net, which enables an algorithm to train itself to perform a task given a prepared training data set.[6] However, according to Barocas and Selbst from Cornell University and UCLA respectively, “an algorithm is only as good as the data it works with."[7] Zarsky, a professor and vice dean at Haifa University, argues that algorithms trained on biased data sets will not only inherit pre-existing biases from the aforementioned data set but also generate novel patterns of unfair bias and discrimination and reinforce these patterns in their decision-making processes.[8] Some surmise an algorithm may even interpret inequalities in historical data as sensible patterns, which in turn supports existing societal biases; furthermore, detecting and addressing unfair bias and discrimination in algorithms for predictive analytics is particularly difficult as these frequently occur because of unintended consequences from using the algorithm and not the purposeful actions of an ill-intentioned programmer.[7]

Transparency

Some assert that transparency as an ethical issue is in opposition to other ethical interests such as privacy.[9] Transparency in algorithms means that the algorithm should not only have its details accessible but also comprehensible to humans analyzing them; accessible information that is not decipherable is no longer useful.[10] Many modern sophisticated artificial intelligence systems are trained via deep learning that is comprised of extensive neural networks reaching up to fifty layers.[6] As each layer adds complexity, Sloan and Warner assert that the human comprehensibility of these networks is affected and thus the transparency.[11]

Predictive Privacy

The term “predictive privacy” refers to the ethical challenges posed by the ability of algorithms to predict sensitive information about an individual using information derived from data sets of other individuals.[5] In 2019, the Electronic Privacy Information Center (EPIC) raised this ethical concern in their official complaint to the Federal Trade Commission (FTC) against HireVue, a recruiting-technology company, stating that “the company’s use of unproven artificial-intelligence systems that scan people’s faces and voices [constitutes] a wide-scale threat to American workers."[3] Mühlhoff’s definition of a violation of predictive privacy is “if sensitive information about [a] person or group is predicted against their will or without their knowledge on the basis of data of many other individuals, provided that these predictions lead to decisions that affect anyone’s...freedom."[5] Some note that predictive privacy can be violated regardless of the prediction’s accuracy, especially when systems for data collection and processing are designed such that subjects cannot provide meaningful or informed consent.[12]

References

  1. ↑ Nyce, Charles. "Predictive Analytics White Paper." The Digital Insurer, American Institute for CPCU, 2007, www.the-digital-insurer.com/wp-content/uploads/2013/12/78-Predictive-Modeling-White-Paper.pdf.
  2. ↑ Cohen, I. G., et al. "The Legal And Ethical Concerns That Arise From Using Complex Predictive Analytics In Health Care." Health Affairs, vol. 33, no. 7, 2014, pp. 1139-47, doi:10.1377/hlthaff.2014.0048.
  3. ↑ 3.0 3.1 Harwell, Drew. "Rights group files federal complaint against AI-hiring firm HireVue, citing ‘unfair and deceptive’ practices." Washington Post, 6 November 2019, www.washingtonpost.com/technology/2019/11/06/prominent-rights-group-files-federal-complaint-against-ai-hiring-firm-hirevue-citing-unfair-deceptive-practices.
  4. ↑ Perry, Walter, et al. "Predictive Policing: The Role Of Crime Forecasting In Law Enforcement Operations." RAND Corporation, 2013, doi:10.7249/rr233.
  5. ↑ 5.0 5.1 5.2 Mühlhoff, Rainer. "Predictive Privacy: Towards An Applied Ethics Of Data Analytics." SSRN, 2020, doi:10.2139/ssrn.3724185.
  6. ↑ 6.0 6.1 6.2 Hardesty, Larry. "Explained: Neural Networks." MIT News, 2021, news.mit.edu/2017/explained-neural-networks-deep-learning-0414.
  7. ↑ 7.0 7.1 Barocas, Solon, and Andrew D. Selbst. "Big Data's Disparate Impact." SSRN, 2016, doi:10.2139/ssrn.2477899.
  8. ↑ Zarsky, Tal Z. "An Analytic Challenge: Discrimination Theory in the Age of Predictive Analytics." I/S: A Journal of Law and Policy, vol. 14.1, 2017, pp. 12-35, kb.osu.edu/bitstream/handle/1811/86702/1/ISJLP_V14N1_011.pdf.
  9. ↑ Canca, Cansu. "Anonymity in the Time of a Pandemic: Privacy vs. Transparency." Bill of Health, Harvard Law, blog.petrieflom.law.harvard.edu/2020/03/30/anonymity-in-the-time-of-a-pandemic-privacy-vs-transparency. Accessed 27 March 2021.
  10. ↑ Mittelstadt, Brent D., et al. "The Ethics Of Algorithms: Mapping The Debate." Big Data & Society, vol. 3, no. 2, 2016, pp. 1-21, SAGE Publications, doi:10.1177/2053951716679679.
  11. ↑ Sloan, Robert H., Richard Warner. "When Is an Algorithm Transparent?: Predictive Analytics, Privacy, and Public Policy." IEEE: Security & Privacy, SSRN, 2017, dx.doi.org/10.2139/ssrn.3051588.
  12. ↑ Schermer, Bart W. "The Limits Of Privacy In Automated Profiling And Data Mining." Computer Law & Security Review, vol. 27, no. 1, 2011, pp. 45-52, doi:10.1016/j.clsr.2010.11.009.
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