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Bias, Jobs, and Fake News

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Abstract

AI devices and programs, when used for facial recognition or for determination of how and to what extent current and former persons convicted of crimes are to be sentenced, are subject to inherent biases. It is well documented that individuals with darker complexions fare major biases by AI programs likely due to lack of data more available to persons of lighter complexion. We discuss the oft-used COMPAS programs which have gone through several evolutions but which lend to possible abuses by defense attorneys’ inability to question the validity of assumptions made and the use of this and other AI-based programs. We then proceed to the very important issue of the extent to which jobs are affected by AI-based robots and programs which potentially have the capability of displacing over half of all jobs presently accomplished by human persons. We end the chapter with the politically charged issue of fake news and AI connection thereto.

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Notes

  1. 1.

    A sampling of the numerous examples include the following: Will Knight, Forget Killer Robots-Bias Is the Real AI Danger, MIT TECHNOLOGY REVIEW (Oct. 3, 2017), https://www.technologyreview.com/s/608986/forget-killer-robotsbias-is-the-real-ai-danger/ and Joy Buolamwini, Artificial Intelligence Has a Problem With Gender and Racial Bias. Here’s How to Solve It, TIME (Feb. 9, 2019), http://time.com/5520558/artificial-intelligence-racial-gender-bias/.

  2. 2.

    Pallab Ghosh, AAAS: Machine learning ‘causing science crisis, BBC (Feb. 18, 2019), https://www.bbc.com/news/science-environment-47267081.

  3. 3.

    Karen Hao, This is how AI bias really happens – and why it’s so hard to fix, MIT TECHNOLOGY REVIEW (Feb. 4, 2019), https://www.technologyreview.com/s/612876/this-is-how-ai-bias-really-happensand-why-its-so-hard-to-fix/.

  4. 4.

    Bias, DICTIONARY.COM, https://www.dictionary.com/browse/bias.

  5. 5.

    Bias versus Variance, ELITEDATASCIENCE, https://elitedatascience.com/bias-variance-tradeoff, and Travis Addair, What is bias in machine learning algorithms? QUORA, https://www.quora.com/What-is-bias-in-machine-learning-alrithms.

  6. 6.

    Alex Guanga, Machine Learning: Bias VS. Variance, MEDIUM, https://becominghuman.ai/machine-learning-bias-vs-variance-641f924e6c57.

  7. 7.

    Two articles quoting Dr. Cheryl Martin, Chief Data Scientist at Alegion are: John K. Waters, AI Bias: It’s in the Data, Not the Algorithm, PURE AI (July 26, 2018), https://pureai.com/articles/2018/07/26/ai-bias-rooted-in-data.aspx, and Alex Woodie, Three Ways Biased Data Can Ruin Your ML Models, DATANAMI (July 18, 2018), https://www.datanami.com/2018/07/18/three-ways-biased-data-can-ruin-your-ml-models/.

  8. 8.

    Sampling bias, SAGE REASEARCHMETHODS, http://methods.sagepub.com/reference/encyclopedia-of-survey-research-methods/n509.xml.

  9. 9.

    Understanding Unconscious Bias: Stereotypes, Prejudice, and Discrimination, CULTUREPLUSCONSULTING, https://cultureplusconsulting.com/2015/05/24/unconscious-bias-stereotypes-prejudice-discrimination/.

  10. 10.

    Systemic value, BUSINESSDICTIONARY, http://www.businessdictionary.com/definition/systemic-value.html.

  11. 11.

    Sensitivity analysis, WIKIPEDIA, https://en.wikipedia.org/wiki/Sensitivity_analysis.

  12. 12.

    Dodd–Frank Wall Street Reform and Consumer Protection Act, Pub. L.111–203, H.R. 4173.

  13. 13.

    FICO score was originally created in 1989 and was the acronym for Fair, Isaac, and Company. It is currently used extensively by the major credit agencies of Experian, Equifax, and TransUnion. Credit score in the United States, WIKIPEDIA, https://en.wikipedia.org/wiki/Credit_score_in_the_United_States.

  14. 14.

    Danielle Keats Citron and Frank Pasquale, The Scored Society: Due Process for Automated Predictions, U. of Md. LEGAL STUDIES RESEARCH PAPER, No. 2014-8, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2376209, 89 Wash. L. Rev. 1 (2014).

  15. 15.

    Tal Z. Zarsky, Understanding Discrimination in the Scored Society, 89 WASH. L. REV. 1375 (2015), http://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1418/89wlr1375.pdf?sequence=1.

  16. 16.

    Id. at 1396–1399 and Solon Barocas and Andrew D. Selbst, Big Data’s Disparate Impact, 104 C. L. REV. 671 (2016), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899.

  17. 17.

    Zarsky supra, note 498 at 1398–1402.

  18. 18.

    State v. Loomis, 881 N.W. 2d 749 (Wis. 2017).

  19. 19.

    Sent to Prison by a Software Program’s Secret Algorithms, NEW YORK TIMES (May 1, 2017), https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-sentencing-bias.html.

  20. 20.

    COMPASS CORE Risk/Needs Assessment and Case Planning, COMPASS CORE, http://www.northpointeinc.com/files/downloads/FAQ_Document.pdf.

    For a detailed discussion of the program see TIM BRENNAN, WILLIAM DIETERICH, and BEATE EHRET, HANDBOOK OF RECIDIVISM RISK/NEEDS ASSESSMENT TOOLS, 1st ed. Ch. 3, WILEY (Nov. 29, 2017), https://onlinelibrary.wiley.com/doi/10.1002/9781119184256.ch3.

  21. 21.

    Id. It should be noted that the authors are from Northpointe Institute for Public Management Inc.

  22. 22.

    Criminal Law – Sentencing Guidelines – Wisconsin Supreme Court Requires Warning before Use of Algorithmic Risk Assessments Sentencing, 120 HARV. L. REV. 1530 (2017), https://harvardlawreview.org/2017/03/state-v-loomis/, citing at 1534, Julia Angwin et al, Machine Bias, PROPUBLICA (May 23, 2016), https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

  23. 23.

    Alexandra Chouldechova, Fair prediction with disparate impact: A study of bias in recidivism prediction instruments, CORNELL CONFERENCE PAPER (Feb. 2017), https://arxiv.org/abs/1703.00056.

  24. 24.

    Angele Christin, Alex Rosenblat, and Danah Boyd, Courts and Predictive Algorithms, DATA&SOCIETY (Oct. 27, 2015), https://datasociety.net/output/data-civil-rights-courts-and-predictive-algorithms/.

  25. 25.

    Sentencing Reform Act of 1984, Pub. L. 98–473, 98 Stat. 1987 (1984). For a detailed commentary, see Federal Sentencing: The Basics, https://www.ussc.gov/sites/default/files/pdf/research-and-publications/research-projects-and-surveys/miscellaneous/201811_fed-sentencing-basics.pdf.

  26. 26.

    Dawinder Sidhu, Moneyball Sentencing, 56 B.C.L.671 (215), http://lawdigitalcommons.bc.edu/bclr/vol56/iss2/6.

  27. 27.

    John Logan Koepke and David G. Robinson, Danger Ahead: Risk Assessment and the Future of Bail Reform, 93 WASH. L. Rev. (rev. Dec. 23, 2018), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3041622.

  28. 28.

    Data & Society is a research institute located in New York City, which, it alleges, is focused on the social and cultural issues arising from data-centric and automated technologies, https://datasociety.net/about/.

  29. 29.

    Courts and Predictive Algorithms, DATA&SOCIETY, https://datasociety.net/output/data-civil-rights-courts-and-predictive-algorithms/.

  30. 30.

    Karen Hao, supra, note 486.

  31. 31.

    Vince Lynch, Three ways to avoid bias in machine learning, TECHCRUNCH (Nov. 6, 2018), https://techcrunch.com/2018/11/06/3-ways-to-avoid-bias-in-machine-learning/.

  32. 32.

    Redlining refers to the practice of banks or other financial lending institutions denying loans to otherwise credit-worthy applicants with respect to the purchase of homes in certain areas that they have marked off-limits by a red line.

  33. 33.

    John Villasenor, Artificial intelligence and bias: Four key challenges, BROOKINGS (Jan. 3, 2019), https://www.brookings.edu/blog/techtank/2019/01/03/artificial-intelligence-and-bias-four-key-challenges/.

  34. 34.

    AI and Bias, IBM, https://www.research.ibm.com/5-in-5/ai-and-bias/.

  35. 35.

    Tobias Baer and Vishnu Kamalnath, Controlling machine-learning algorithms and their biases, MCKINSEY & COMPANY (Nov. 2017), https://www.mckinsey.com/business-functions/risk/our-insights/controlling-machine-learning-algorithms-and-their-biases.

  36. 36.

    Stas Sajin, Preventing Machine Learning Bias, TOWARDSDATASCIENCE (Oct. 31, 2018), https://towardsdatascience.com/preventing-machine-learning-bias-d01adfe9f1fa.

  37. 37.

    Jayshree Pandya, Can Artificial Intelligence Be Biased?, FORBES (Jan. 20, 2019), https://www.forbes.com/sites/cognitiveworld/2019/01/20/can-artificial-intelligence-be-biased/.

  38. 38.

    Anupam Chandler, The Racist Algorithm?, 115 MICH. L. REV. 1023 (2017) at 1039–1045, http://michiganlawreview.org/wp-content/uploads/2017/04/115MichLRev1023_Chander.pdf.

  39. 39.

    Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort, Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms, U. of MICH.CONFERENCE PAPER (May 22, 2014), http://www-personal.umich.edu/~csandvig/research/Auditing%20Algorithms%20%2D%2D%20Sandvig%20%2D%2D%20ICA%202014%20Data%20and%20Discrimination%20Preconference.pdf.

  40. 40.

    S. (unassigned), 116th Cong. (2019), Algorithmic Accountability Act of 2019, https://www.wyden.senate.gov/imo/media/doc/Algorithmic%20Accountability%20Act%20of%202019%20Bill%20Text.pdf?utm_campaign=the_algorithm.unpaid.engagement&utm_source=hs_email&utm_medium=email&utm_content=71709273&_hsenc=p2ANqtz-9E4AfpVeN6rZlMn-sZ6KfITloTxQWGYinXF-cCrW15Zz5OF12kVVm78ky5hq9uufsx_MJHyjG6bXM4YgOmzukvAJ4q4w&_hsmi=71709273.

  41. 41.

    The Algorithm, MIT TECH REVIEW (April 12, 2019), newsletters@technologyreview.com.

  42. 42.

    City Council Passes First Bill in Nation to Address Transparency, Bias in Government Use in Algorithms, NYCLU (Dec. 11, 2017), https://www.nyclu.org/en/press-releases/city-council-passes-first-bill-nation-address-transparency-bias-government-use.

  43. 43.

    Brian Merchant, PepsiCo Is ‘Relentlessly Automating’ Its Workforce and It’s Even More Dystopian Than It Sounds, GIZMODO (Feb. 27, 2019), https://gizmodo.com/pepsico-is-relentless-automating-its-workforce-and-it-1832804035.

  44. 44.

    Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation, MCKINSEY GLOBAL INSTITUTE (Dec. 2017), https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Future%20of%20Organizations/What%20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages/MGI-Jobs-Lost-Jobs-Gained-Report-December-6-2017.ashx.

  45. 45.

    Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages, MCKINSEY GLOBAL INSTITUTE (Nov. 2017), https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages.

  46. 46.

    Rob Price, Stephen Hawking: Automation and AI is going to decimate middle class jobs, BUSINESS INSIDER (Dec. 2, 2016), https://www.businessinsider.com/stephen-hawking-ai-automation-middle-class-jobs-most-dangerous-moment-humanity-2016-12.

  47. 47.

    Rene Millman, Bank of England warns of large-scale job losses from AI, INTERNET OF BUSINESS (Aug. 20, 2018), https://internetofbusiness.com/bank-of-england-joins-chorus-of-disapproval-about-ai-job-losses/.

  48. 48.

    Calum McClelland, The Impact of Artificial Intelligence – Widespread Job Losses, IOT FOR ALL (Aug. 17, 2018), https://www.iotforall.com/impact-of-artificial-intelligence-job-losses/.

  49. 49.

    Martin Kelly, Historical Significance of the Cotton Gin, THOUGHTCO (March 5, 2018), https://www.thoughtco.com/the-cotton-gin-in-american-history-104722.

  50. 50.

    Glenn Luk, Technology Has Already Taken Over 90% of the Jobs Humans Used to Do., FORBES (Jan. 18, 2018), https://www.forbes.com/sites/quora/2018/01/18/technology-has-already-taken-over-90-of-the-jobs-humans-used-to-do/#529511c21bdd.

  51. 51.

    Industrial Revolution, WIKIPEDIA, https://en.wikipedia.org/wiki/Industrial_Revolution. A lengthy history may be found in Technological unemployment, WIKIPEDIA, https://en.wikipedia.org/wiki/Technological_unemployment.

  52. 52.

    James Vincent, Automation threatens 800 million jobs, but technology could still save us, says report, THE VERGE (Nov. 30, 2017), https://www.theverge.com/2017/11/30/16719092/automation-robots-jobs-global-800-million-forecast.

  53. 53.

    Kiran Garimella, Job Loss From AI? There’s More to Fear!, FORBES (Aug. 7, 2018), https://www.forbes.com/sites/cognitiveworld/2018/08/07/job-loss-from-ai-theres-more-to-fear/#17781bbb23eb.

  54. 54.

    Carl Benedikt Frey and Michael A. Osborne, The Future of Employment: How Susceptible Are Jobs To Computerization?, https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf.

  55. 55.

    Id. at 36–42, 57–72.

  56. 56.

    Frey, supra, note 537 at 57–72.

  57. 57.

    Melanie Arntz, Terry Gregory, and Ulrich Zierahn, The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis, OECD SOCIAL, EMPLOYMENT AND MIGRATION WORKING PAPERS, No. 189, OECD Publishing, Paris, https://doi.org/10.1787/5jlz9h56dvq7-en.

  58. 58.

    Daron Acemoglu and Pascual Restrepo, Robots and Jobs: Evidence From U.S. Labor Markets, MIT Working Paper 17-04 (March 17, 2014) at 32–33, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2941263.

  59. 59.

    Martin Ford, RISE OF THE ROBOTS: TECHNOLOGY AND THE THREAT OF A JOBLESS FUTURE, BASIC BOOKS (2015).

  60. 60.

    Osonde A. Osoba and William Welser IV, The Risks of Artificial Intelligence to Security and the Future of Work, RAND Corporation (2017), https://www.rand.org/pubs/perspectives/PE237.html.

  61. 61.

    Aaron Smith and Janna Anderson, AI, Robotics, and the Future of Jobs, PEW RESEARCH CENTER (Aug. 6, 2014), https://www.pewinternet.org/2014/08/06/future-of-jobs/.

  62. 62.

    Austin James Marsh, Employment Practices and Future Technologies – Taking the Human Out of Human Resources, LEXOLOGY (March 29, 2019), https://www.lexology.com/library/detail.aspx?g=993edd9f-3142-4e4f-916b-a944b02d4b5e.

  63. 63.

    Matthew Lieberman, Why the Real AI Jobs Issue Isn’t Unemployment, FORBES (Feb. 14, 2018), https://www.forbes.com/sites/forbestechcouncil/2018/02/14/why-the-real-ai-jobs-issue-isnt-unemployment/.

  64. 64.

    Kiran Garimella, Job Loss from AI? There’s More to Fear!, FORBES (Aug. 1, 2018), https://www.forbes.com/sites/cognitiveworld/2018/08/07/job-loss-from-ai-theres-more-to-fear/#49ef7f7723eb.

  65. 65.

    John Loeffler, The Rise of AI and Employment: How Jobs Will Change to Adapt, SQUARE TERMINAL (Nov. 4, 2018), https://interestingengineering.com/the-rise-of-ai-and-employment-how-jobs-will-change-to-adapt. The articles cited statistics of PWC UK Economic Outlook Report, http://pwc.to/1iTbYJZ.

  66. 66.

    Ben Halder, How China’s ‘Cobot’ Revolution Could Transform Automation, OZY (March 24, 2019), https://www.ozy.com/fast-forward/how-chinas-cobot-revolution-could-transform-automation/93044.

  67. 67.

    Rich Barlow, Economist predicts job loss to machines, but see long-term hope, PHYS ORG (March 19, 2018), https://phys.org/news/2018-03-economist-job-loss-machines-long-term.html.

  68. 68.

    See, e.g., Tamsin McMahon, Why economists can’t predict the future, MACLEAN’S (Feb. 11, 2014), https://www.macleans.ca/economy/economicanalysis/why-economists-cant-predict-the-future/. Some argue economists cannot explain the past as well as the future, Ben Chu, Economists do not predict the future and can’t explain the past, INDEPENDENT (Aug. 17, 2014), https://www.independent.co.uk/news/business/comment/economists-do-not-predict-the-future-and-cant-explain-the-past-9673649.html.

  69. 69.

    See contrasting arguments by Carl Benedikt Frey and Robert D. Atkinson, Will AI Destroy More Jobs Than It Creates Over the Next Decade? THE WALL STREET JOURNAL (April 2, 2019) at R4.

  70. 70.

    Future Work/Technology 2050 Global Scenarios, THE MILLENNIUM PROJECT (July 23, 2018), http://www.millennium-project.org/future-work-technology-2050-global-scenarios/.

  71. 71.

    See also the discussion by Calum McClelland, The Impact of Intelligence – Widespread Job Losses, IOT FOR ALL (Aug. 17, 2018), https://www.iotforall.com/impact-of-artificial-intelligence-job-losses/.

  72. 72.

    Russian interference in the 2016 United States elections, WIKIPEDIA, https://en.wikipedia.org/wiki/Russian_interference_in_the_2016_United_States_elections.

  73. 73.

    Michael Tomz and Jessica L.P. Weeks, Public Opinion and Foreign Electoral Intervention, (Paper, Am. Pol. Sci. Assc. Aug. 2018), https://web.stanford.edu/~tomz/working/TomzWeeks-ElectoralIntervention-2018-08-24.pdf; and Hunt Allcott and Matthew Gentzkow, Social Media and Fake News in the 2016 Election, NAT. BUR. OF ECO RESEARCH WORKING PAPER, No. 23089 (rev. April 2017), http://www.nber.org/papers/w23089.

  74. 74.

    Samuel C. Woolley and Douglas Guilbeault, “Computational Propaganda in the United States of America: Manufacturing Consensus Online”; and Samuel Woolley and Philip N. Howard, Eds. WORKING PAPER 2017.5. Oxford, UK: Project on Computational Propaganda, http://comprop.oii.ox.ac.uk/.

  75. 75.

    Samantha Bradshaw and Philip N. Howard, Troops, Trolls and Troublemakers: A Global Inventory of Organized Social Media Manipulation, WORKING PAPER 2017.12. Oxford, UK: Project on Computational Propaganda.

  76. 76.

    Jonathan Zittrain, Engineering An Election, 127 HARV. L.R. FORUM 335 (June, 2014), http://harvardlawreview.org/wp-content/uploads/2014/06/vol127_Symposium_Zittrain.pdf.

  77. 77.

    Vyacheslav Polonski, Artificial Intelligence Has the Power to Destroy or Save Democracy, blog, COUNCIL OF FOREIGN RELATIONS (Aug. 7, 2017), https://www.cfr.org/blog/artificial-intelligence-has-power-destroy-or-save-democracy.

  78. 78.

    Alleged Russian political meddling documented in 27 countries since 2004, USATODAY (Sept. 7, 2017), https://www.usatoday.com/story/news/world/2017/09/07/alleged-russian-political-meddling-documented-27-countries-since-2004/619056001/.

  79. 79.

    Olivia Beavers and Jacqueline Thomsen, Russia election meddling fears expand to other countries, THE HILL (Aug. 25, 2017), https://thehill.com/policy/cybersecurity/403559-russia-election-meddling-fears-expand-to-other-countries.

  80. 80.

    Read the criminal complaint against Russian charged with election interference, CNN (Oct. 19, 2018), https://www.cnn.com/2018/10/19/politics/criminal-complaint-elena-alekseevna-khusyaynova-russia/index.html.

  81. 81.

    Department of Justice 2018 Election Security Fact Sheet, DEPT. OF JUSTICE (Nov. 3, 2018), https://www.justice.gov/opa/pr/department-justice-2018-election-security-fact-sheet.

  82. 82.

    Monroe Doctrine, WIKIPEDIA, https://en.wikipedia.org/wiki/Monroe_Doctrine.

  83. 83.

    Daryl Worthington, The USA and Latin America: A History of Meddling, NEW HISTORIAN (April 12, 2015), https://www.newhistorian.com/the-usa-and-latin-america-a-history-of-meddling/3476/.

  84. 84.

    Caitlin Oprysko, John Bolton threatens to tighten the screws on Venezuela, POLITICO (March 6, 2019), https://www.politico.com/story/2019/03/06/foreign-bank-sanctions-nicolas-maduro-1207019.

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Girasa, R. (2020). Bias, Jobs, and Fake News. In: Artificial Intelligence as a Disruptive Technology. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-35975-1_6

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