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Economics and Law of Artificial Intelligence

Abstract

Al technologies affect the center of private autonomy and its limits, the notion of a contract and its interpretation, the equilibrium of parties’ interests, the structure and means of enforcement, the effectiveness of legal and contractual remedies, and the vital attributes of the legal system of effectiveness, fairness, impartiality, and predictability. The increasing global investments in blockchain technology justify a progressive regulatory adaptation to the altering materiality and so, civil liability and the insurance sector are required to amend and govern an ever-more pressing techno-economic evolution. It is worth noting that adapting existing rules to deal with the technology will need an understanding of the various manners robots and humans respond to legal rules. A robot cannot make an instinctive judgment about the value of a human life. It is argued that the automation of legal services is a manner to enhance access to justice, diminish legal costs, and upgrade the rule of law, which means that these improvements are a democratization of law. There is a shifting role of artificial intelligence in the legal course.

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Notes

  1. 1.

    UNCITRAL Model L. On Electronic Commerce with Guide to Enactment, U.N. Sales No E.99.V.4 (1996); UNCITRAL Model L. on Electronic Signatures with Guide to Enactment, U.N. Sales No E.02.V.8 (2001); UNCITRAL, U.N. Convention on the use of Electronic Communications In International Contracts, U.N. Sales No. E.07.V.2 (2005).

  2. 2.

    Martin Arnold, ‘Davos: Blockchain can no longer be ignored’ (Financial Times, 24 January 2018). https://www.ft.com/content/c0794556-ff50-11e7-9650-9c0ad2d7c5b5. Primavera De Filippi and Aaron Wright, Blockchain and the Law (HUP 2018) 33–57; Michèle Finck, Blockchain Regulation and Governance in Europe (CUP 2019) 10–33.

  3. 3.

    https://www.bloomberg.com/news/articles/2018-02-01/hackers-in-500-million-heist-targeted-obscurecryptocurrency. NEM (XEM) is the tenth global electronic currency for value.

  4. 4.

    David Harvey, Collisions in the Digital Paradigm. Law and Rule-Making in the Internet Age (Hart Publishing 2017). Karen Yeung, ‘Regulation by Blockchain: The Emerging Battle for Supremacy between the Code of Law and Code as Law’ (2019) 82 MLR 207.

  5. 5.

    Christina Mulligan, Revenge against Robots, 69 SC L Rev 579, 588–89 (2018) (“If it turns out that punishing robots provides the right kind of psychological benefit to humans following an injury, we should punish robots.”).

  6. 6.

    Saurwein/Just/Latzer, Governance of algorithms: options and limitations, (2015) 17(6) Info 35.

  7. 7.

    Wagner, Ethics as an Escape from Regulation: From Ethics-Washing to Ethics-Shopping? in: Hildebrandt (ed.), Being Profiling. Cogitas ergo sum, 2008, pp. 108 et seq. Nemitz, Constitutional democracy and technology in the age of artificial intelligence, (2018) Phil. Trans. R. Soc. A 376.

  8. 8.

    Daniel Martin Katz, Quantitative Legal Prediction—or—How I Learned to Stop Worrying and Start Preparing for the Data-Driven Future of the Legal Services Industry, 62 Emory L.J. 909, 939–41 (2013) (discussing predictive technologies used in patent litigation).

  9. 9.

    Anthony J. Casey & Anthony Niblett, The Death of Rules and Standards, 92 Ind. L.J. 1401, 1403 (2017).

  10. 10.

    John O. McGinnis, Machines v. Lawyers, City J. (2014), https://www.city-journal.org/html/machines-v-lawyers-13639.html.

  11. 11.

    Magali Sarfatti Larson, The Rise Of Professionalism 3 (1977).

  12. 12.

    Anthony D’Amato, Can/Should Computers Replace Judges? 11 Ga. L. Rev. 1277, 1300–01 (1977) (presenting computerization as a path to a more “determinable legal system”).

  13. 13.

    Civic tech is the use of technology by governments to stimulate positive interactions among citizens themselves, and between citizens and their state.

  14. 14.

    Mayur Patel et al., The Emergence of Civic Tech 6–7 (2013); see also Michael Halberstam, Beyond Transparency: Rethinking Election Reform from an Open Government Perspective, 38 Seattle U. L. Rev. 1007, 1009–10 (2015) (discussing Open Government movement to increase transparency).

  15. 15.

    Eric Niiler, Can AI Be a Fair Judge in Court? Estonia Thinks So, Wired (Mar. 25, 2019), https://www.wired.com/story/can-ai-be-fair-judge-court-estonia-thinks-so. Mireille Hildebrandt, Smart Technologies And The End(S) Of Law (2015).

  16. 16.

    Anthony J. Casey & Anthony Niblett, Self-Driving Laws, 66 U. Toronto L.J. 429, 435 (2016).

  17. 17.

    Frank Pasquale & Glyn Cashwell, Prediction, Persuasion, and the Jurisprudence of Behaviorism, 68 U. Toronto L.J. 63, 80 (2018). Frank Pasquale & Glyn Cashwell, Four Futures of Legal Automation, 63 UCLA L. Rev. Discourse 26, 28 (2015).

  18. 18.

    Sarah B. Lawsky, Formalizing the Code, 70 Tax L. Rev. 377, 379 (2017).

  19. 19.

    Ozkan Eren & Naci Mocan, Abstract, Emotional Judges and Unlucky Juveniles (Nat’l Bureau of Econ. Research, Working Paper No. 22611, 2016), http://www.nber.org/papers/w22611.pdf (finding that unexpected losses in “football games played by a prominent college team in the state … increase disposition (sentence) lengths assigned by judges during the week following the game”).

  20. 20.

    J.C. Smith, Machine Intelligence and Legal Reasoning, 73 Chi.-Kent L. Rev. 277, 278 (1998) (“From the perspective of the lawyer, we have the concept of the rule of law, as contrasted with the rule of persons; thus, in some sense.”).

  21. 21.

    Margaret Jane Radin, Reconsidering the Rule of Law, 69 B.U. L. Rev. 781, 781 (1989) (explaining that “[t]he ideal of ‘the rule of law, not of men’ calls upon us to strive to ensure that our law itself will rule (govern) us, not the wishes of powerful individuals.”

  22. 22.

    Pedro Domingos, The Master Algorithm 12–20 (2015) (discussing how integration of five schools of machine learning may lead to rapid advances in computing).

  23. 23.

    Margaret Jane Radin, Reconsidering the Rule of Law, 69 B.U. L. Rev. 781, 781 (1989).

  24. 24.

    Craig Lambert, Shadow Work 201–50 (2016) (discussing shadow work on the internet, such as offering free information through customer reviews that may be monetized through, for example, Zagat guides).

  25. 25.

    How the Legal Industry Is Evolving, STEELCASE (June 2010), https://www.steelcase.com/research/articles/topics/collaboration/a-new-legal-brief/.

  26. 26.

    Boosting Procurement Efficiency Through Automation, SYNERGIST.IO (June 29, 2018), https://synergist.io/procurement-automation/ (automating procurement led to “reducing legal spending in this area by 75%”).

  27. 27.

    David Howarth, Is Law a Humanity (or Is it More Like Engineering)? 3 Arts & Human. Higher Educ. 9, 11–12 (2004). David Howarth, Law As Engineering 61–73 (2014).

  28. 28.

    David Golumbia, The Cultural Logic Of Computation 78 (2009).

  29. 29.

    Andrew Guthrie Ferguson, Big Data and Predictive Reasonable Suspicion, 163 U. PA. L. REV. 327, 350–51 (2015), and Elizabeth E. Joh, Policing Police Robots, 64 UCLA L. Rev. Discourse 516, 519 (2016).

  30. 30.

    John O. McGinnis & Russell G. Pearce, The Great Disruption: How Machine Intelligence Will Transform the Role of Lawyers in the Delivery of Legal Services, 82 Fordham L. Rev. 3041, 3041–42 (2013). Richard Susskind & Daniel Susskind, The Future of the Professions 202 (2015) (describing computerized drafting of legal and other documents).

  31. 31.

    Benjamin Alarie, The Path of the Law: Towards Legal Singularity, 66 U. Toronto L.J. 443, 445 (2016); Benjamin Alarie et al., Law in the Future, 66 U. Toronto L.J. 423, 427–28 (2016). Tom Meltzer, Robot Doctors, Online Lawyers and Automated Architects: the Future of the Professions?, GUARDIAN (June 15, 2014, 2:00 PM), https://www.theguardian.com/technology/2014/jun/15/robot-doctors-online-lawyers-automated-architects-future-professionsjobs-technology.

  32. 32.

    Richard H. Fallon, Jr., “The Rule of Law” as a Concept in Constitutional Discourse, 97 Colum. L. Rev. 1, 5 (1997) (describing these “ideal types … as (i) historicist, (ii) formalist, (iii) Legal Process, and (iv) substantive”).

  33. 33.

    Martin Ford, Rise Of The Robots 1–27 (2015). Ray Kurzweil, The Age Of Spiritual Machines 2–4 (2000); Nick Srnicek & Alex Williams, Inventing The Future: Post capitalism And A World Without Work 2–3 (2015).

  34. 34.

    Paul F. Kirgis, The Knowledge Guild: The Legal Profession in an Age of Technological Change, 11 Nev. L.J. 184, 184 (2010) ((“Susskind offers no evidence to support his claim that greater automation of legal work will result in less demand for human legal services. In fact, the evidence suggests that productivity increases in knowledge industries increase demand for those knowledge goods.”). Frank Pasquale, Two Concepts of Immortality: Reframing Public Debate on Stem Cell Research, 14 Yale J.L. & Human. 73, 75–76 (2002) (critiquing the “downloading” of memory, intellect, and will onto hardware or software).

  35. 35.

    Richard H. Fallon, Jr., “The Rule of Law” as a Concept in Constitutional Discourse, 97 Colum. L. Rev. 1, 5 (1997) Fallon’s list of aspects of Legal Process conceptions of the rule of law is drawn from the locus classicus of the Legal Process approach. Id. (citing Henry M. Hart, Jr. & Albert M. Sacks, The Legal Process 4–5, 152–53, 157–58, 695 (William N. Eskridge, Jr. & Philip P. Frickey eds., 1994)).

  36. 36.

    Donald A. Dripps, Justice Harlan on Criminal Procedure: Two Cheers for the Legal Process School, 3 Ohio St. J. Crim. L. 125, 126 (2005); Laura Kalman, The Strange Career Of Legal Liberalism 20 (1996) (describing emergence of the Legal Process school).

  37. 37.

    Case C-131/12 Google Spain SL v Mario Costeja González [2014] ECLI:EU:C:2014:317, para 47, https://eurlex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A62012CJ0131.

  38. 38.

    Jürgen Kühling, ‘Rückkehr des Rechts: Verpflichtung von “Google & Co.” zu Datenschutz’ [2014] Europäische Zeitschrift für Wirtschaftsrecht 527.

  39. 39.

    The High Court’s Brexit Decision: A Lesson in Constitutional Law for the UK Government, in: Verfassungsblog–On matters constitutional, 3. November 2016, https://verfassungsblog.de/the-high-courts-brexit-decision-a-lesson-inconstitutional-law-for-the-uk-government/. Judgments in cases C-355/10, ECLI:EU:C:2012:516, European Parliament v. Council, para 64ff, https://eurlex.europa.eu/legalcontent/EN/TXT/?qid=1534186617433&uri=CELEX:62010CJ0355, and C-293/12 and 594/12/, ECLI:EU:C:2014:238, Digital Rights Ireland, para 54ff, http://curia.europa.eu/juris/documents.jsf?num=C-293/12.

  40. 40.

    Microsoft Says AI Advances Will Require New Laws, Regulations, Bloomberg 18. January 2018, https://www.bloomberg.com/news/articles/2018-01-18/microsoft-says-ai-advances-will-require-new-laws-regulations.

  41. 41.

    Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), OJ L 119, 4.5.2016, p. 1–88, https://eur-lex.europa.eu/eli/reg/2016/679/oj.

  42. 42.

    John Locke, Of Civil Government Second Treatise 67, 68, Introduction by Russell Kirk, (Henry Regnery 1955).

  43. 43.

    Gunther Teubner, Law As An Autopoietic System 45–46 (1993). Niklas Luhmann, Law As A Social System 464 (trans. Klaus A. Ziegert, 2004).

  44. 44.

    Lawrence M. Friedman, American Law 257 (1984) “Law carries a powerful stick: the threat of force. This is the fist inside its velvet glove.”

  45. 45.

    Dicey, A.V, Introduction to the Study of the Law of the Constitution, London: Macmillian and Co., Limited. 1920.

  46. 46.

    Marbury v. Madison, 5 U.S. 137 (1803) (treating the constitution as legally enforceable and as the highest law of the land)., Richard H. Fallon. Jr.. “The Rule of Law” as a Concept in Constitutional Discourse”, 97 Colum. L. Rev. 1. 7 (1997), at 1 (“The Rule of Law is a much celebrated, historical ideal, the precise meaning of which may be less clear today than ever before.”).

  47. 47.

    Thomas O. Sargentich, The Contemporary Debate About Legislative-Executive Separation of Powers, 72 Corn. L. Rev. 430, 450 (1987) (“A close relation between the rule of law and separation of powers is evident in both the liberal and democratic elements of liberal democratic theory.”).

  48. 48.

    Stare decisis is a legal doctrine that compels courts to follow historical cases when making a ruling on a similar case making certain that cases with similar scenarios and facts are approached in the same way which means that courts have to follow legal precedents set by previous decisions.

  49. 49.

    Paul Lippe, Daniel Martin Katz, and Dan Jackson, ‘Legal by Design: A New Paradigm for Handling Complexity in Banking Regulation and Elsewhere in Law’, Oregon Law Review 93, no. 4 (2015), http://papers.ssrn.com/abstract=2539315.

  50. 50.

    G Marcus and E Davis, Rebooting AI: Building Artificial Intelligence We Can Trust (Pantheon 2019); D Spiegelhalter, The Art of Statistics: Learning from Data (Pelican 2019) 143–187.

  51. 51.

    M Loughlin, ‘The erosion of sovereignty’ (2016).

  52. 52.

    T O’Reilly, ‘Government as a Platform’ (2010) 6 Innovations 1. M Goede, ‘E-Estonia: The e-government cases of Estonia, Singapore, and Curaçao‘(2019) 8 Archives of 20 Business Research 2.

  53. 53.

    Mueller, J. P.; Massaron, L. Artificial intelligence. Hoboken: John Wiley & Sons, 2018.

  54. 54.

    MB Chalmers, ‘SmartLaw 2.0: The new future of law’ (Lexology, 22 August 2018). M Tegmark, Life 3.0: Being Human in the Age of Artificial Intelligence (Allen Lane 2017) 105.

  55. 55.

    RA Posner, How Judges Think (Harvard University Press 2008).

  56. 56.

    E Volokh, ‘Chief Justice Robots’ (2019) 68 Duke Law Journal 1135, 1138.

  57. 57.

    G Sartor and LK Branting, ‘Introduction: Judicial Applications of Artificial Intelligence’ in G Sartor and LK Branting (eds) Judicial Applications of Artificial Intelligence (Springer 1998).

  58. 58.

    Alarie, Benjamin, The Path of the Law: Toward Legal Singularity (May 27, 2016). https://doi.org/10.2139/ssrn.2767835.

  59. 59.

    P Stein, Legal Evolution: The Story of an Idea (Cambridge University Press 2009); S Deakin, ‘Legal Evolution: Integrating Economic and Systemic Approaches, ‘University of Cambridge Faculty of Law Research Paper No. 41/2011 (2011).

  60. 60.

    J Habermas, On the Logic of the Social Sciences (1967) cited in W Outhwaite, Habermas: Key Contemporary Thinkers (second ed, Polity Press 2009). H Ross, Law as a Social Institution (Hart 2001).

  61. 61.

    P Bourdieu, Language and Symbolic Power (Harvard University Press 1991).

  62. 62.

    HLA Hart, The Concept of Law (third ed, Oxford University Press 2012).

  63. 63.

    State of Wisconsin v Loomis 881 N.W.2d 749 (Wis. 2016); H-W Liu, C Fu and Y-J Chen, ‘Beyond State v Loomis: Artificial Intelligence, Government Algorithmization and Accountability’ (2019) 27 International Journal of Law and Information Technology 2; R Yu and RS Alo, ‘What’s Inside the Black Box? AI Challenges for Lawyers and Researchers’ (2019) 19 Legal Information Management 1.

  64. 64.

    M Hildebrandt, “Law as computation in the era of artificial legal intelligence: Speaking law to the power of statistics” (2017).

  65. 65.

    Williams v. New York, 337 U.S. 241, 251 (1949) (describing the “grave responsibility” of sentencing).

  66. 66.

    Eric Holder, Att’y Gen., U.S. Dep’t of Justice, Address at the National Association of Criminal Defense Lawyers 57th Annual Meeting and 13th State Criminal Justice Network Conference (Aug. 1, 2014), https://www.justice.gov/opa/speech/attorney-general-eric-holder-speaks-national-association-criminal-defense-lawyers-57th (cautioning that recidivism risk assessments “may exacerbate unwarranted and unjust disparities”).

  67. 67.

    State v. Loomis 881 N.W.2d 749 (Wis. 2016) Wisconsin Supreme Court Requires Warning Before Use of Algorithmic Risk Assessments in Sentencing. The Wisconsin Supreme Court’s mandated PSI advisement is a critical—though likely ineffectual—acknowledgement of the potential problems of algorithmic risk assessments. https://harvardlawreview.org/2017/03/state-v-loomis/.

  68. 68.

    M Annany and K Crawford, ‘Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability’ (2018) 20 New Media & Society 3; J Pearl and D MacKenzie, The Book of Why: The New Science of Cause and Effect (Penguin 2018) 358.

  69. 69.

    P Boucher, ‘Leibniz: What Kind of Legal Rationalism?’ in M Dascal (ed) Leibniz: What Kind of Rationalist? (Springer 2008).

  70. 70.

    CP Wells, ‘Langdell and the Invention of Legal Doctrine‘(2010) 28 Buffalo Law Review 3. CC Langdell, Cases on Contracts (Little Brown & Co. 1871), vi. MH Hoeflich, ‘Langdell and the Invention of Legal Doctrine,’ (1986) 30 The American Journal of Legal 83 History 2.

  71. 71.

    OW Holmes, The Common Law (Little Brown & Co. 1881) 1. OW Holmes, ‘The Path of the Law’ (1897) 110 Harvard Law Review 5, 997. MH Fisch, ‘Justice Holmes, the Prediction Theory of Law, and Pragmatism’ (1942) 39 The Journal of Philosophy 4; RA Posner, ‘The Path Away from the Law‘(1996) 110 Harvard Law Review 1039.

  72. 72.

    S Brewer, ‘Law, Logic, and Leibniz: A Contemporary Perspective,‘ in A Artosi, B Pieri and G Sartor (eds) Leibniz: Logico-Philosophical Puzzles in the Law (Springer 2013).

  73. 73.

    R Pound, ‘Mechanical Jurisprudence,‘ Columbia Law Review 8 no. 605 (1908).

  74. 74.

    SR Ratner, ‘Legal Realism School,‘ in R Wolfrum (ed) Max Planck Encyclopedia of Public International Law 89 Online (Oxford University Press, 2007).

  75. 75.

    KN Llewellyn, The Bramble Bush: The Classic Lectures on the Law and Law School (Oxford University Press, [1930] 2008).

  76. 76.

    J Dewey, ‘Logical Method and Law’ (1924) 10 Cornell Law Review 1, 25. C Gray, The Nature and Sources of Law (Columbia University Press 1909); R Pound, ‘Justice According to Law’ (1914) 1 The Midwest Quarterly 3.

  77. 77.

    J McCarthy, ‘Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part 94 I‘(1960), http://www-formal.stanford.edu/jmc/recursive.pdf; cf. SL Andersen, ‘John McCarthy: Father of AI‘(2002) 17 IEEE Intelligent Systems 5. J McCarthy, ‘Philosophical and Scientific Presuppositions of Logical AI, ‘in HJ Levesque and F Pirri (eds).

  78. 78.

    Hart, The Concept of Law, 124–154. WG Popp and B Schlink, ‘Judith, A Computer Program to Advise Lawyers in Reasoning a Case’ (1974) 15 Jurimetrics Journal 303.

  79. 79.

    MZ Bell, ‘Why Expert Systems Fail’ (1985) 36 Journal of the Operational Research Society 7 (1985).

  80. 80.

    P Leith, ‘Fundamental Errors in Legal Logic Programming‘(1986) 29 The Computer Journal 6. M Polanyi, The Tacit Dimension (Rev ed, University of Chicago Press 2009) 3–25.

  81. 81.

    JE Laird, C Lebiere and PS Rosenbloom, ‘A Standard Model of the Mind: Toward a Common Computational Framework Across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics’ (2017) 38 AI Magazine 4.

  82. 82.

    David Spiegelhalte, The Art of Statistics: Learning from Data (Pelican Books) March 28, 2019.

  83. 83.

    PS Churchland, “Can Neurobiology Teach Us Anything About Self Consciousness?’ (1994) 42 Proceedings and Addresses of the American Philosophical Association 3; M Allen and KJ Friston, ‘From cognitivism to autopoiesis: towards a computational framework for the embodied mind’ (2018) 195 Synthese 6 (2018).

  84. 84.

    John MacCormick, What Can Be Computed?: A Practical Guide to the Theory of Computation, 2018 Princeton University Press.

  85. 85.

    J Schmidhüber, ‘Algorithmic Theories of Everything’ (2000) https://arxiv.org/abs/quant-ph/0011122.

    P Hut, M Alford and M Tegmark, ‘On Math, Matter and Mind’ (2006) 36 Foundations of Physics 6.

  86. 86.

    L Floridi, ‘Against Digital Ontology’ (2009) 168 Synthese 1. J Smithies, The Digital Humanities and the Digital Modern (Palgrave 2017) Hildebrandt, Mireille, Law as Computation in the Era of Artificial Legal Intelligence: Speaking law to the power of statistics. In: University of Toronto Law Journal, Vol. 68, No. 1, 2, 2018, p. 12–35.

  87. 87.

    See Alain Supiot’s identification of (the ‘anthropological function’ of law as a ‘technique [for the] humanization of technology,’ contend that the replacement of juridical reasoning with computation would ultimately result in the subordination of the ‘rule of law’ to a new ‘rule of technology’.) Homo Juridicus: On the Anthropological Function of Law By Alain Supiot. London, New York: Verso, 2007.

  88. 88.

    Ryan Calo, Robotics and the Lessons of Cyberlaw, 103 Cal. L. Rev. 513, 515, 549–62 (2015). Alicia Solow-Niederman, Emerging Digital Technology and the “Law of the Horse,” UCLA L. Rev. Disc.: Law Meets World (2019), https://www.uclalawreview.org/emerging-digital-technology-and-the-law-ofthe-horse.

  89. 89.

    Deirdre K. Mulligan & Kenneth A. Bamberger, Saving Governance-by-Design, 106 Calif. L. Rev. 697, 705–14 (2018) (offering detailed literature review of science, technology, and society (STS) theory and its salience for public regulation).

  90. 90.

    Cary Coglianese & David Lehr, Transparency and Algorithmic Governance, 71 Admin. L. Rev. 1, 2 n.2 (2019) Peter Dizikes, AI, the Law, and Our Future, MIT News Office (Jan. 18, 2019), http://news.mit.edu/2019/first-ai-policy-congress-0118.

  91. 91.

    Chris Weller, A California Police Department is Using Software to Decide if You’re About to Commit a Crime, Bus. Insider (Jan. 12, 2016), https://www.businessinsider.com/intrado-beware-system-tracks-threat-levels-2016-1.

  92. 92.

    Daniel L. Chen, Machine Learning and the Rule of Law 4–7 (Working Paper, 2019), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3302507.

  93. 93.

    Shai Danziger, Jonathan Levav, and Liora Avnaim-Pesso, “Extraneous Factors in Judicial Decisions,” Proceedings of the National Academy of Sciences of the United States of America 108, no. 17 (April 26, 2011): 6889–92, https://doi.org/10.1073/pnas.1018033108.

  94. 94.

    Solon Barocas and Andrew D. Selbst, “Big Data’s Disparate Impact,” California Law Review 104, no. 3 (2016): 671–732, https://doi.org/10.15779/z38bg31.

  95. 95.

    Ewert v. Canada, 2018 SCC 30. Note, however, that other tools—such as the Ontario Domestic Assault Risk Assessment Tool (“ODARA”)—are in extensive use in Canada and courts are relying on these tools, R v. Beharri, 2015 ONSC 5900; R v. Primmer 2017 ONSC 2953.

  96. 96.

    UDHR art. 2, Julia Dressel and Hany Farid, “The Accuracy, Fairness, and Limits of Predicting Recidivism,” Science Advances 4, no. 1 (January 1, 2018), https://doi.org/10.1126/sciadv.aao5580.

  97. 97.

    Dave Orr and Colin Rule, Artificial Intelligence and the Future of Online Dispute Resolution http://www.newhandshake.org/SCU/ai.pdf (“We are still a long way away from giving an AI Lexis-Nexis access and then asking it to serve on the Supreme Court.”). AI NOW INST., AI NOW 2019 Report (2019), https://ainowinstitute.org/AI_Now_2019_Report.pdf (concerns about the “black box” nature of algorithms and the potential for harm and abuse if they are used by government agencies without fully accounting for built-in biases).

  98. 98.

    Rashida Richardson, Jason M. Schultz & Kate Crawford, Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, And Justice, 94 N.Y.U. L.REV. 192, 192 (2019) Ali Winston & Ingrid Burrington, A Pioneer in Predictive Policing Is Starting a Troubling New Project, The Verge (Apr. 27, 2018), https://www.theverge.com/2018/4/26/17285058/predictive-policing-predpol-pentagonai-racial-bias.

  99. 99.

    Yuval Noah Harari, ‘Who Will Win the Race for AI?’, Foreign Policy Magazine (Washington, Winter 2019).

  100. 100.

    Commission, ‘Artificial Intelligence for Europe’ (Communication) COM (2018) 237 final.

  101. 101.

    Tania Rabesandratana, ‘Europe moves to compete in global AI arms race’ [2018] 350 Science 474. Andrew Philip Hunter, Lindsay Sheppard et al., ‘Artificial Intelligence and National Security–The Importance of the AI Ecosystem’ [2018] Centre for Strategic and International Studies. PwC, ‘PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution’ (2017); Deloitte, ‘Artificial Intelligence Innovation Report,’ (2018) and McKinsey Global Institute, ‘Notes from the AI Frontier: Modelling the Impact of AI on the World Economy’ (September 2018).

  102. 102.

    J. Abbruzzese, ‘Elizabeth Warren calls to break up Facebook, Google and Amazon’, NBC 8 March 2019, available at www.nbcnews.com/tech/tech-news/elizabeth-warren-calls-break-facebook-google-amazonn980911.

  103. 103.

    L. Vaas, ‘FTC renews call for single federal privacy law’, Naked Security by Sophos 10 May 2019, available at https://nakedsecurity.sophos.com/2019/05/10/ftc-renews-call-for-single-federal-privacy-law/.

  104. 104.

    Torres v. N. Am. Van Lines, Inc., 658 P.2d 835, 838–39 (Ariz. Ct. App. 1982).

  105. 105.

    Sarah O’Meara, China’s Ambitious Quest to Lead the World in AI by 2030, 572 Sci. Am. 427, 428 (2019).

  106. 106.

    Mark A. Geistfeld, A Roadmap for Autonomous Vehicles: State Tort Liability, Automobile Insurance, and Federal Safety Regulation, 105 Calif. L. Rev. 1611, 1632–74 (2017).

  107. 107.

    Bryant Walker Smith, Automated Driving and Product Liability, 2017 Mich. St. L. Rev. 1, 32–56).

  108. 108.

    Jack Boeglin, The Costs of Self-Driving Cars: Reconciling Freedom and Privacy with Tort Liability in Autonomous Vehicle Regulation, 17 Yale J.L. & Tech. 171, 174–75, 185–201 (2015).

  109. 109.

    Intel Corp. v. Hamidi, 71 P.3d 296, 308 (Cal. 2003) (declining to find that emails from a former employee to numerous current employees criticizing the company’s employment practices could, despite their unauthorized nature, constitute trespass to chattels). Mariano-Florentino Cuéllar, Administrative War, 82 Geo. Wash. L. Rev. 1343, 1439 (2014) (discussing how ideological norms and the common law appeared to buttress each other and reinforced concerns about government ownership of industry during wartime mobilization in the early 1940s).

  110. 110.

    Wright v. Ford Motor Co., 508 F.3d 263, 268–74 (fifth Cir. 2007) (applying Texas law, which in this context establishes a rebuttable presumption that a manufacturer is not liable for a design defect when it complied with applicable federal safety regulations). Dan B. Dobbs, Paul T. Hayden & Ellen M. Bublick, The Law of Torts § 167 (2d ed. 2019) (explaining the use and effects of presumptions in tort law). Regents of the Univ. of Cal. v. Superior Court, 413 P.3d 656, 663–74 (Cal. 2018) (finding that universities have a duty to protect students from reasonably foreseeable harms).

  111. 111.

    Ajay Agrawal, Joshua Gans & Avi Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence 7–20 (2018); Mark Muro, Robert Maxim & Jacob Whiton, Metro. Policy Program, Brookings Inst., Automation and Artificial Intelligence: How Machines Are Affecting People and Places 29–46 (2019).

  112. 112.

    Restatement (Second) of Torts § 435.

  113. 113.

    G Zekos, Finance Crimes: Insider Trading and Money Laundering, 2020 Nova Science Publications New York USA. www.novapublishers.com Tom C.W. Lin, The New Market Manipulation, 66 Emory L.J. 1253, (2017); Renato Zamagna, The Future of Trading Belong to Artificial Intelligence, Medium (Nov. 15, 2018), https://medium.com/datadriveninvestor/the-future-of-trading-belong-to-artificial-intelligencea4d5887cb677. Council Regulation 596/2014 of 16 Apr. 2014, On Market Abuse (Market Abuse Regulation) Council Directive 2014/57/EU of 16 Apr. 2014, On Criminal Sanctions for Market Abuse (Market Abuse Directive), 2014 O.J. (L. 173) 179.

  114. 114.

    Nicky Burridge, Artificial Intelligence Gets a Seat in the Boardroom, Nikkei Asia Rev. (May 10, 2017), https://asia.nikkei.com/Business/Artificial-intelligence-gets-a-seatin-the-boardroom.

  115. 115.

    Mark Fenwick, Wulf A. Kaal & Erik P.M. Vermeulen, The “Unmediated” and “Tech-Driven” Corporate Governance of Today’s Winning Companies, 16 N.Y.U. J.L. & BUS. 75, 114–15 (2019).

  116. 116.

    Edward L. Pittman, Quantitative Investment Models, Errors, and the Federal Securities Laws, 13 N.Y.U. J.L. & BUS. 633, 643–44 (2017) Sam Ransbotham et al., Reshaping Business With Artificial Intelligence 14 (2017), https://www.bcg.com/Images/Reshaping%20Business%20with%20Artificial%20Intelligence_tcm9-177882.pdf.

  117. 117.

    Jeffrey Dastin, Exclusive: Amazon Rolls out Machines That Pack Orders and Replace Jobs, Reuters (May 13, 2019, 6:08 AM), https://www.reuters.com/article/us-amazon-com-automation-exclusive/exclusive-amazonrolls-out-machines-that-pack-orders-and-replace-jobs-idUSKCN1SJ0X1.

  118. 118.

    H. James Wilson & Paul R. Daugherty, Collaborative Intelligence: Humans and AI Are Joining Forces, HARV. BUS. REV., July–Aug. 2018, at 114.

  119. 119.

    Sonia K. Katyal, Private Accountability in the Age of Artificial Intelligence, 66 UCLA L. REV. 54, 65 (2019).

  120. 120.

    Mark A. Geistfeld, A Roadmap for Autonomous Vehicles: State Tort Liability, Automobile Insurance, and Federal Safety Regulation, 105 Calif. L. Rev. 1611, 1614–16, 1620 (2017) (“Autonomous vehicles will not be perfectly safe.”); Mark A. Lemley & Bryan Casey, Remedies for Robots, 86 U. Chi. L. Rev 1311, 1313 (2019) (“As robotics and artificial intelligence (AI) systems increasingly integrate into our society, they will do bad things.”).

  121. 121.

    V.S. Khanna, Corporate Criminal Liability: What Purpose Does It Serve?, 109 Harv. L. Rev. 1477, 1489–90 (1996).

  122. 122.

    United States v. Athlone Indus., Inc., 746 F.2d 977, 979 (3d Cir. 1984).

  123. 123.

    Ugo Pagallo, Killers, Fridges, and Slaves: A Legal Journey in Robotics, 26 AI & SOC’Y 347, 349 (2011).

  124. 124.

    United States v. Wise, 370 U.S. 405, 416 (1962) (“[A] corporate officer is subject to prosecution under § 1 of the Sherman Act whenever he knowingly participates in effecting the illegal contract, combination, or conspiracy… .”). Iowa Code Ann. § 321.277 (Westlaw through 2019 legislation) (defining the offense of “reckless driving”).

  125. 125.

    CFPB and DOJ Order Ally To Pay $80 Million to Consumers Harmed by Discriminatory Auto Loan Pricing, CFPB Newsroom (Dec. 20, 2013), https://www.consumerfinance.gov/aboutus/newsroom/cfpb-and-doj-order-ally-to-pay-80-million-to-consumers-harmed-by-discriminatoryauto-loan-pricing/.

  126. 126.

    Solon Barocas & Andrew D. Selbst, Big Data’s Disparate Impact, 104 Calif. L. Rev. 671, 711–12, 726 (2016); Stephanie Bornstein, Antidiscriminatory Algorithms, 70 Ala. L. Rev. 519, 535 (2018).

  127. 127.

    Restatement (Third) Of Agency § 2.04 cmt. b.

  128. 128.

    United States v. Bank of New England, N.A., 821 F.2d 844, 856 (first Cir. 1987). Model Penal Code § 2.07(1)(c) (AM. LAW INST. 2018).

  129. 129.

    Mihailis E. Diamantis, Corporate Criminal Minds, 91 Notre Dame L. Rev. 2049, 2056–58 (2016).

  130. 130.

    Jack M. Balkin, The Three Laws of Robotics in the Age of Big Data, 78 Ohio St. L.J. 1217, 1234 (2017).

  131. 131.

    Angie Schmitt, Uber Got off the Hook for Killing a Pedestrian with Its Self-Driving Car, STREETSBLOG USA (Mar. 8, 2019), https://usa.streetsblog.org/2019/03/08/uber-got-off-the-hookfor-killing-a-pedestrian-with-its-self-driving-car/.

  132. 132.

    Cindy R. Alexander & Mark A. Cohen, The Causes of Corporate Crime: An Economic Perspective, in Prosecutors In The Boardroom: Using Criminal Law To Regulate Corporate Conduct 11, 14–15, 17 (Anthony S. Barkow & Rachel E. Barkow eds., 2011).

  133. 133.

    Frank Pasquale, Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society, 78 Ohio St. L.J. 1243, 1244–45 (2017) (describing North Carolina’s attempt to prohibit legal software manufacturers from attaining a lower standard of liability than attorneys and the Federal Trade Commission and Department of Justice’s criticism of North Carolina’s decision).

  134. 134.

    Mihailis E. Diamantis, The Extended Corporate Mind: When Corporations Use AI to Break the Law, 98 N.C. L. Rev. 893 (2020). Available at: https://scholarship.law.unc.edu/nclr/vol98/iss4/6. p 930.

  135. 135.

    Pamela H. Bucy, Corporate Ethos: A Standard for Imposing Corporate Criminal Liability, 75 Minn. L. Rev. 1095, 1146 (1991) (“[M]ost corporations use at least one form of compensation, indemnification, in a way that encourages corporate crime… .”); David R. Cohen & Roberta D. Anderson, Insurance Coverage for “Cyber-Losses,” 35 Tort & Ins. L.J. 891, 926 (2000) (“Directors’ and officers’ [(“D&O”)] insurance policies afford coverage for the defense and indemnification costs of directors and officers sued in connection with discharge of their corporate duties. A typical D&O policy insures against any ‘loss’ arising out of a ‘wrongful act.’”).

  136. 136.

    David C. Vladeck, Machines Without Principals: Liability Rules and Artificial Intelligence, 89 Wash. L. Rev. 117, 125 (2014) (discussing the difficulties in determining who is responsible for AI errors). Matilda Claussén-Karlsson, Artificial Intelligence And The External Element Of The Crime 22 (2017), http://oru.diva-portal.org/smash/get/diva2:1115160/FULLTEXT01.pdf.

  137. 137.

    UNCITRAL, UNCITRAL Model L. On Secured Transactions, U.N. Sales No. E.17.V.1 (2016).

  138. 138.

    Teresa Rodríguez de las Heras Ballell, Legal Aspects of Recommender Systems in the Web 2.0: Trust, Liability and Social Networking, in Recommender Systems for the Social Web 43 (Pazos Arias et al. eds., 2012) (according to the theory of “layers of electronic intermediation,” intermediaries provide accessibility, visibility, and credibility services). Jorge Feliu Rey, El Derecho de Garantías Mobiliarias en Contexto: Una Aproximación Global, 29 LA LEY MERCANTIL, 1, 6 (2016) (explaining how the diverging evolution of legal traditions—formalist and functionalist—on secured transactions has also been associated with a different role of registry and, accordingly, to differing registry models—title registry versus notice filing).

  139. 139.

    Harry Surden, Values Embedded in Legal Artificial Intelligence (U. of Colorado Law Legal Studies Research Paper No. 17-17, 2017), https://ssrn.com/abstract=2932333.

  140. 140.

    European Parliament, How Blockchain Technology Could Change Our Lives (2017), http://www.europarl.europa.eu/RegData/etudes/IDAN/2017/581948/EPRS_IDA(2017)581948_EN.pdf; Joseph Bonneau, et al., SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies, IEEE SYMP. ON SECURITY & PRIVACY, 104, 118 (2015), https://www.ieee-security.org/TC/SP2015/papers-archived/6949a104.pdf (analyzing the multiple functionalities of Bitcoin).

  141. 141.

    Int’l Telecomm. Union, Series Y: Global Information Infrastructure, Internet Protocol Aspects And Next-Generation Networks (2012), http://www.itu.int/rec/T-REC-Y.2060-201206-I (The Internet of Things (IoT) has been defined as “a global infrastructure for the information society, enabling advanced services by interconnecting [physical and virtual] things based on existing and evolving interoperable information and communication technologies”); INT’L TELECOMM. UNION, ITU-T SG20: IOT And Its Applications Including Smart Cities And Communities (SC&C), http://www.itu.int/en/ITU-T/studygroups/2013-2016/20 (The Global Standards Initiative on Internet of Things (IoT-GSI) concluded its activities in July 2015 following TSAG decision to establish the new Study Group 20).

  142. 142.

    King et al., Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions, (2019) Science and Engineering Ethics, https://doi.org/10.1007/s11948-018-00081-0.

  143. 143.

    “Artificial Intelligence and Criminal Law Responsibility In Council Of Europe Member States-The Case Of Autonomous Vehicles.” European Committee on Crime Problems (CDPC). September 14, 2018. https://rm.coe.int/cdpc-2018-14-artificial-intelligence-and-criminal-law-project-2018-202/16808d6d09.

  144. 144.

    “Faultless Responsibility: On the Nature and Allocation of Moral Responsibility for Distributed Moral Actions.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://royalsocietypublishing.org/doi/10.1098/rsta.2016.0112.

  145. 145.

    Arjun Kharpal, Robot with $100 bitcoin buys drugs, gets arrested, CNBC (Apr. 22, 2018), https://www.cnbc.com/2015/04/21/robot-with-100-bitcoin-buys-drugs-gets-arrested.html.

  146. 146.

    21 U.S.C. § 841(a)(1) (criminalizing distribution and possession with intent to distribute a controlled substance). Matt Novak, Was This The First Robot Ever Arrested?, GIZMODO (Feb. 18, 2014), https://paleofuture.gizmodo.com/was-this-the-first-robot-ever-arrested-1524686968 (describing police confiscation in 1982 of a robot: “The police considered citing [its owner] for failing to obtain a permit for advertising … but no charges were filed and the robot was ultimately returned.”).

  147. 147.

    18 U.S.C. § 2(a) (making it a crime to aid and abet offenses).

  148. 148.

    Christopher Markou, We Could Soon Face A Robot Crimewave: The Law Needs to Be Ready, The Conversation (April 11, 2017), https://theconversation.com/we-could-soon-face-a-robot-crimewave-the-lawneeds-to-be-ready-75276.

  149. 149.

    Gabriel Hallevy, The Criminal Liability of Artificial Intelligence Entities: The Criminal Liability of Artificial Intelligence Entities—From Science Fiction to Legal Social Control, 4 Akron Intellectual Property Journal 171, 191 (2010). Christina Mulligan, Revenge Against Robots, 69 S. Carolina. L. Rev. 579, 580 (2018).

  150. 150.

    Alex Sarch, Who Cares What You Think? The Irrelevance of Unmanifested Mental States, 36 L. & PHIL. 707 (2017).

  151. 151.

    Model Penal Code § 2.07 (outlining conditions under which a corporation could be convicted of an offense).

  152. 152.

    Ryan Abbott, Everything is Obvious, 66 UCLA L. REV 4, 23–28 (2019).

  153. 153.

    Daisuke Wakabayashi, Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam, N.Y TIMES (Mar. 19, 2018), https://www.nytimes.com/2018/03/19/technology/uber-driverless-fatality.html 30 See, e.g., http://aiweirdness.com/post/174691534037/why-did-the-neural-network-cross-the-road (discussing the programmer who made her machine learning algorithm attempt to tell jokes).

  154. 154.

    Chris DeBrusk, The Risk of Machine Learning Bias (and How to Prevent It), MIT Sloan Management REVIEW (2018), https://sloanreview.mit.edu/article/the-risk-of-machine-learning-bias-and-how-to-prevent-it/.

  155. 155.

    Samuel Falkon, The Story of the DAO—Its History and Consequences, The Startup (Dec. 24, 2017), https://medium.com/swlh/thestory-of-the-dao-its-history-and-consequences-71e6a8a551ee (The DAO would function according to smart contract, or pre-programmed rules dictating future behaviour). Vincent C. Müller & Nick Bostrom, Future Progress in Artificial Intelligence: A Survey of Expert Opinion, in Fundamental Issues of Artificial Intelligence 553 (Vincent C. Müller ed., 2016).

  156. 156.

    Jesus Rodriguez, Consciousness and the Weak vs. Strong AI Debate, Towards Data Science (August 23, 2018), https://towardsdatascience.com/g%C3%B6del-consciousness-and-the-weak-vs-strong-ai-debate-51e71a9189ca Ryan Abbott, The Reasonable Computer: Disrupting the Paradigm of Tort Liability, 86 Geo. Wash. L. Rev. 1 (2018).

  157. 157.

    Jeff Gurney, Driving into the Unknown: Examining the Crossroads of Criminal Law and Autonomous Vehicles, 5 Wake Forrest J. L. & Policy 393, 433 (2015).

  158. 158.

    Liu, Yingqi, et al. “Trojaning attack on neural networks.” (2018). Shokri, Reza, et al. “Membership inference attacks against machine learning models.” 2017 IEEE Symposium on Security and Privacy (SP) for one example. Fredrikson, Matthew, et al. “Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.” USENIX Security Symposium. 2014.

  159. 159.

    Obie Okuh, Comment, When Circuit Breakers Trip: Resetting The CFAA To Combat Rogue Employee Access, 21 Alb. L.J. Sci. & Tech. 637, 645 (2011). Matthew Ashton, Note, Debugging The Real World: Robust Criminal Prosecution In The Internet of Things, 59 Ariz. L. Rev. 805, 813 (2017). Computer Fraud and Abuse Act, 18 U.S.C.A. § 1030 (2008).

  160. 160.

    United States v. Kramer, 631 F.3d 900 (eighth Cir. 2011). Fink v. Time Warner Cable, 810 F. Supp. 2d 633 (S.D.N.Y. 2011). United States v. Carlson, No. 05-3562, 209 Fed.Appx. 181 (3d Cir. 2006).

  161. 161.

    Pulte Homes, Inc. v. Laborers’ Int’l Union of N. Am., 648 F.3d 295 (sixth Cir. 2011). Facebook, Inc. v. Power Ventures, Inc., 844 F.3d 1058 (ninth Cir. 2016). United States v. Drew, 259 F.R.D. 449 (C.D. Cal. 2009). Derek E. Bambauer & Oliver Day, The Hacker’s Aegis, 60 Emory L.J. 1051, 1105 (2011).

  162. 162.

    Cass R. Sunstein, Algorithms, correcting biases, 86 Soc. Res. Int. Q. 499–511 (2019); Joshua A. Kroll et al., Accountable Algorithms (2016), http://papers.ssrn.com/abstract=2765268; Virginia Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, And Punish The Poor (2018); Katherine J. Strandburg, Rulemaking and Inscrutable Automated Decision Tools, 119 Columbia Law Rev. 1851–1886 (2019); Crystal Yang & Will Dobbie, Equal Protection Under Algorithms: A New Statistical and Legal Framework, SSRN 3462379 (2019); Thomas Nachbar, Algorithmic Fairness, Algorithmic Discrimination, Va. Public Law Leg. Theory Res. Pap. (2020).

  163. 163.

    Sandra Wachter, Brent Mittelstadt, & Chris Russell Why Fairness Cannot Be Automated: Bridging The Gap Between Eu Nondiscrimination Law and AI https://ssrn.com/abstract=3547922, p1.

  164. 164.

    Jenna Burrell, How the Machine “Thinks:” Understanding Opacity in Machine Learning Algorithms, Big Data Soc. (2016). Brent Mittelstadt, From Individual to Group Privacy in Big Data Analytics, 30 Philos. Technol. 475–494 (2017); Sandra Wachter, Affinity Profiling and Discrimination by Association in Online Behavioural Advertising, 35 Berkeley Technol. Law J. (2020), https://papers.ssrn.com/abstract=3388639.

  165. 165.

    European Commission, Non-discrimination, European Commission—European Commission (2020), https://ec.europa.eu/info/aid-development-cooperation-fundamentalrights/your-rights-eu/know-your-rights/equality/non-discrimination_en The Racial Equality Directive (2000/43/EC), The Gender EqualityDirective (recast) (2006/54/EC), The Gender Access Directive (2004/113/EC),47 and the Employment Directive (2000/78/EC).

  166. 166.

    Sandra Wachter, Brent Mittelstadt & Chris Russell, Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR, 3 Harv. J. Law Technol. 841–887 (2018); Marion Oswald & Alexander Babuta, Data Analytics and Algorithmic Bias in Policing (2019).

  167. 167.

    Sandra Wachter & B. D. Mittelstadt, A right to reasonable inferences: re-thinking data protection law in the age of Big Data and AI, 2019 Columbia Bus. Law Rev. (2019).

  168. 168.

    Sandra Wachter, Brent Mittelstadt, & Chris Russell Why Fairness Cannot Be Automated: Bridging The Gap Between Eu Nondiscrimination Law And AI https://ssrn.com/abstract=3547922 P 64.

  169. 169.

    Gary Edmond & Kristy A. Martire, Just Cognition: Scientific Research on Bias and Some Implications for Legal Procedure and Decision-Making, 82 Mod. Law Rev. 633–664 (2019).

  170. 170.

    H. L. A. Hart, Positivism and the Separation of Law and Morals, 71 Harv. Law Rev. 593 (1958). Deborah Hellman, When Is Discrimination Wrong? 4 (2008).

  171. 171.

    Ricci v. DeStefano, 557 U.S. 579.

  172. 172.

    European Union Agency For Fundamental Rights & Council Of Europe, Handbook On European Non-Discrimination Law 43 (2018 Edition).

  173. 173.

    Evelyn Ellis & Philippa Watson, Eu Anti-Discrimination Law 163–168 (2012).

  174. 174.

    Roel Dobbe et al., A Broader View on Bias in Automated Decision Making: Reflecting on Epistemology and Dynamics, in Proceedings Of The 2018 Workshop On Fairness, Accountability And Transparency In Machine Learning (2018).

  175. 175.

    Henriette Cramer et al., Assessing and addressing algorithmic bias in practice, 25 INTERACTIONS 58–63, 60 (2018) Association for Computing Machinery Digital Library, Bias in Computer Systems, ACM.ORG, https://dl.acm.org/doi/10.1145/230538.230561.

  176. 176.

    Batya Friedman & Helen Nissenbaum, Bias in Computer Systems, 14 ACM TRANS. INF. SYST. 330–347 (1996).

  177. 177.

    Anupam Chander, “The Racist Algorithm?,” Mich. Law Review 115, no. 6 (2017): 1023, http://michiganlawreview.org/wp-content/uploads/2017/04/115MichLRev1023_Chander.pdf.

  178. 178.

    Personal Information Protection and Electronic Documents Act, S.C. 2000, c. 5 (revised June 23, 2015), Schedule 1 Principle 4.9 (“Upon request, an individual shall be informed of the existence, use, and disclosure of his or her personal information and shall be given access to that information. An individual shall be able to challenge the accuracy and completeness of the information and have it amended as appropriate.).

  179. 179.

    Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC, 2016 O.J. (L. 119), art. 19 (“The data subject shall have the right to obtain from the controller without undue delay the rectification of inaccurate personal data concerning him or her. Taking into account the purposes of the processing, the data subject shall have the right to have incomplete personal data completed, including by means of providing a supplementary statement.”).

  180. 180.

    Algorithmic Accountability Act of 2019, H.R. 2231, 116th Cong. (2019).

  181. 181.

    Texas Department of Housing and Community Affairs v. Inclusive Communities Project, Inc., 135 S. Ct. 2507 (2015). Civil Rights Act of 1964, 42 U.S.C. § 2000e-2(a); Age Discrimination in Employment Act of 1967, 29 U.S.C. § 623(a). Hales v. Casey’s Marketing Co., 886 F.3d 730 (2018) “To prevail on a hostile work environment claim, a plaintiff must prove the following five elements: … (3) that a causal nexus existed between the harassment and protected group status.”

  182. 182.

    Miranda Bogen et al., Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination, PROC. OF THE 2020 Conf. On Fairness, Accountability, and Transparency 492, 498, 499 (2020). (Describing how companies, especially in highly regulated industries, are unlikely to collect data on sensitive attributes for antidiscrimination efforts due to concerns about exposing themselves to liability).

  183. 183.

    Galit Shmueli, To Explain or to Predict?, 25 Statist. Sci. 289 (2010).

  184. 184.

    Thomas J. Campbell, Regression Analysis in Title VII Cases: Minimum Standards, Comparable Worth, and Other Issues Where Law and Statistics Meet, 36 Stanford L. Rev. 1299 (1984). Ann Morning & Daniel Sabbagh, From Sword to Plowshare: Using Race for Discrimination and Antidiscrimination in the United States, 57 Int. Soc. Sci. J. 57 (2005) (looking at use of statistics, but also making broader arguments about the racist history of data acquisition).

  185. 185.

    Alexandra Chouldechova et al., A Case Study of Algorithm-Assisted Decision Making in Child Maltreatment Hotline Screening Decisions, PROC. OF THE 2018 Conf. On Fairness, Accountability, And Transparency (2018) (discussing an algorithmic system deployed to evaluate child abuse and neglect risk in Allegheny County). Mikella Hurley & Julius Adebayo, Credit Scoring in the Era of Big Data, 18 Yale J. L. & Tech. 148 (2017).

  186. 186.

    Harry Surden, ‘The Ethics of Artificial Intelligence in Law: Basic Questions’ in M Dubber and F Pasquale (eds), The Oxford Handbook of AI Ethics (Oxford University Press 2019).

  187. 187.

    Hin-Yan Liu, ‘The Power Structure of Artificial Intelligence’ (2018) 10 Law, Innovation and Technology 197. Jamie Susskind, Future Politics: Living Together in a World Transformed by Tech (Oxford University Press 2018).

  188. 188.

    Robert Cover et al. Narrative, Violence, and the Law, The Essays of Robert Cover (Michigan: University of Michigan Press, 1995). Jacques Derrida, ‘Force of Law: The “Mystical Foundation of Authority”’, Cardozo Law Review 11 (1990): 920–1045.

  189. 189.

    M Hildebrandt, ‘The Force of Law and the Force of Technology’, in The Routledge International Handbook of Technology, Crime and Justice, ed. M.R.P. McGuire and Holt (Routledge, 2017), 579–608.

  190. 190.

    Christopher Markou and Simon Deakin, Ex Machina Lex: The Limits of Legal Computability at: https://ssrn.com/abstract=3407856.

  191. 191.

    Contos G, Guyton J, Langetieg P, Vigil M. 2011. Individual taxpayer compliance burden: The role of assisted methods in taxpayers response to increasing complexity. In IRS Research Bulletin: Proceedings of the IRS Research Conference 2010, pp. 191–220. Washington, D.C.: IRS.

  192. 192.

    ‘Legal Tech Market Map: 50 Startups Disrupting the Legal Industry’, CB Insights—Blog, 13 July 2016, https://www.cbinsights.com/blog/legal-tech-market-map-company-list/. A. Ramanathan et al., ‘Integrating Symbolic and Statistical Methods for Testing Intelligent Systems: Applications to Machine Learning and Computer Vision’, in 2016 Design, Automation Test in Europe Conference Exhibition (DATE), 2016, 786–91.

  193. 193.

    Federico Cabitza, ‘The Unintended Consequences of Chasing Electric Zebras’ (IEEE SMC Interdisciplinary Workshop HUML 2016, The Human Use of Machine Learning, 12/16/2016, Venice, Italy, 2016), https://www.researchgate.net/publication/311702431_The_Unintended_Consequences_of_Chasing_Electric_Zebras.

  194. 194.

    B.C. Brosnahan, “The Law and Computers” (1970) 1(3) Auckland U. L. Rev. 1 at 2: “no matter how many contingencies the program may be designed to cope with, a computer can never outdo humans and perform activities that cannot be analysed into logical patterns.” JC Smith, “Machine Intelligence and Legal Reasoning” (1998) 73 ChicagoKent Law Review 277.

  195. 195.

    Bob Ambrogi, The 20 Most Important Legal Technology Developments of 2018, LAWSITES (Dec. 26, 2018).

  196. 196.

    Alarie, Benjamin, The Path of the Law: Toward Legal Singularity (May 27, 2016). https://doi.org/10.2139/ssrn.2767835.

  197. 197.

    Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford University Press, 2014).

  198. 198.

    Richard Susskind, Tomorrow’s Lawyers: An Introduction To Your Future (2d ed. 2017). Frank Pasquale, A Rule of Persons, Not Machines: The Limits of Legal Automation, 87 Geo. Wash. L. Rev. (2019).

  199. 199.

    Michael Culpan, “This Algorithm Can Create an Imitation Van Gogh in 60 Minutes,” Wired, September 1, 2015, https://www.wired.co.uk/article/art-algorithm-recreates-paintings.

  200. 200.

    Mark Coeckelbergh, “Can Machines Create Art?,” Philosophy & Technology 30 (2017): 285–6. George E. Lewis, “From Network Band to Ubiquitous Computing: Rich Gold and the Social Aesthetics of Interactivity,” in Improvisation and Social Aesthetics eds. Georgina Born, Eric Lewis and Will Straw (Durham and London: Duke University Press, 2017).

  201. 201.

    Dana Remus and Frank Levy, “Can Robots Be Lawyers? Computers, Lawyers, and the Practice of Law,” The Georgetown Journal of Legal Ethics 30 (2017): 501–58.

  202. 202.

    Gunter Lösel, “Can Robots Improvise?,” Liminalities: A Journal of Performance Studies 14, no. 1 (2018).

  203. 203.

    Roger Dean, Hyperimprovisation: Computer-Interactive Sound Improvisation (Wisconsin: A-R Editions, Inc., 2003), xiii.

  204. 204.

    George E. Lewis, “(Machine) Listening as Improvisation,” Technosphere Magazine, December 23, 2018), https://www.technosphere-magazine.hkw.de/p/5-Rainbow-Family-5Aj9nAxzG6zFRAAd9icEvH. George E. Lewis, “Listening for Freedom with Arnold Davidson,” Critical Inquiry 45, no. 2 (Winter 2019): 434.

  205. 205.

    Daniel Martin Katz, ‘The MIT School of Law? A Perspective on Legal Education in the 21st Century’, University of Illinois Law Review, no. 5 (2014). On the lack of methodological integrity of the use of statistics in social science e.g. https://errorstatistics.com/2016/11/08/gigerenzer-at-the-psa-how-fisherneyman-pearson-bayes-were-transformed-into-the-null-ritual-comments-and-queries-i/, adversarial statistics: https://jasp-stats.org.

  206. 206.

    Jason Koebler, ‘Rise of the Robolawyers’, The Atlantic, April 2017, https://www.theatlantic.com/magazine/archive/2017/04/rise-of-the-robolawyers/517794/.

  207. 207.

    Nicholas Carr, The Glass Cage: Automation and Us, 1 edition (New York: W. W. Norton & Company, 2014). John Searle, ‘Minds, Brains, and Programs’, Behavioral and Brain Sciences 3, no. 3 (1980): 517–57.

  208. 208.

    UN Press Release, World Stumbling Zombie-Like into a Digital Welfare Dystopia, Warns UN Human Rights Expert (Oct. 17, 2019). Report of the Special Rapporteur on Extreme Poverty and Human Rights, UN Doc. A/74/48037, para. 8 (Oct. 11, 2019).

References

  1. Casey, A. J., & Niblett, A. (2016). Self-driving laws. 66 University of Toronto Law Journal, 429, 435.

    Google Scholar 

  2. Casey, A. J., & Niblett, A. (2017). The death of rules and standards. 92 Indiana Law Journal, 1401, 1403.

    Google Scholar 

  3. Golumbia, D. (2009). The cultural logic of computation. Cambridge: Harvard University Press.

    Book  Google Scholar 

  4. Harvey, D. (2017). Collisions in the digital paradigm: Law and rule-making in the internet age. Oxford: Hart Publishing.

    Google Scholar 

  5. Zekos, G. (2020). Finance crimes: insider trading and money laundering. New York, USA: Nova Science Publications.

    Book  Google Scholar 

  6. Hart, H. L. A. (2012). The concept of law (3rd ed.). Oxford: Oxford University Press.

    Book  Google Scholar 

  7. Locke, J. (1955). Of civil government second treatise 67, 68, introduction by Russell Kirk. Washington: Henry Regnery.

    Google Scholar 

  8. MacCormick, J. (2018). What can be computed?: A practical guide to the theory of computation. Princeton: Princeton University Press.

    Book  Google Scholar 

  9. Yeung, K. (2019). Regulation by blockchain: The emerging battle for supremacy between the code of law and code as law. Modern Law Review, 82, 207.

    Article  Google Scholar 

  10. Hildebrandt, M. (2017). Law as computation in the era of artificial legal intelligence: Speaking law to the power of statistics. Amsterdam: Elsevier.

    Google Scholar 

  11. Loughlin, M. (2016). The erosion of sovereignty. Netherlands Journal of Legal Philosophy, 2, 57–81.

    Article  Google Scholar 

  12. Arnold, M. (January, 2018). Davos: Blockchain can no longer be ignored (Financial Times, 24 January 2018).

    Google Scholar 

  13. McKinsey Global Institute. (September, 2018). Notes from the AI frontier: Modelling the impact of AI on the world economy.

    Google Scholar 

  14. Mueller, J. P., & Massaron, L. (2018). Artificial intelligence. Hoboken: John Wiley & Sons.

    Google Scholar 

  15. Burridge, N. (May 10, 2017). Artificial intelligence gets a seat in the boardroom, Nikkei Asia Review.

    Google Scholar 

  16. Boucher, P. (2008). Leibniz: What Kind of legal rationalism? In M. Dascal (Ed.), Leibniz: What kind of rationalist? New York: Springer.

    Google Scholar 

  17. Kirgis, P. F. (2010). The knowledge guild: The legal profession in an age of technological change. Nevada Law Journal, 11, 184.

    Google Scholar 

  18. Posner, R. A. (2008). How judges think. Cambridge: Harvard University Press.

    Google Scholar 

  19. Deakin, S. (2011). Legal evolution: Integrating economic and systemic approaches, University of Cambridge Faculty of Law Research Paper No. 41/2011.

    Google Scholar 

  20. UNCITRAL Model Law on Electronic Commerce With Guide to Enactment. (1996). United Nations Publication Sales No E.99.V.4.

    Google Scholar 

  21. Harari, Y. N. (2019). Who will win the race for AI?, Foreign Policy Magazine. Washington: Winter.

    Google Scholar 

Download references

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    Georgios I. Zekos

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Zekos, G.I. (2021). AI and Legal Issues. In: Economics and Law of Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-64254-9_10

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