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I, Inhuman Lawyer: Developing Artificial Intelligence in the Legal Profession

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Robotics, AI and the Future of Law

Part of the book series: Perspectives in Law, Business and Innovation ((PLBI))

Abstract

What are the possibilities of having AI lawyers in the true sense—as autonomous, decision-making agents that can legally advise us or represent us? This chapter delves into the problems and possibilities of creating such systems. This idea is inevitably faced with a multitude of challenges, among them the challenge of translating law into an algorithm being the most fundamental for the beginning of the creation of an AI lawyer. Moreover, the chapter examines the linguistic aspects of such a translation and later moves on into the ethical aspect of creating such lawyers and ethically codifying their conduct. This is followed by a brief deliberation on whether Asimov’s Three Laws of Robotics would be helpful in this regard. The ethical discussion results in a proposal for a concept of Fairness by Design, conceived as the minimum standard for ethical behavior instilled in all AI agents. The chapter also attempts to give a general overview of the current state-of-the-art AI technologies employed in the legal domain as well as imagines the future of AI in Law . Subsequently, the chapter imagines an AI agent deal with and resolve the “Solomon test” of splitting the baby. Finally, it is concluded that the advantage of having AI lawyers can be measured by the possibility of redefining the legal profession in its entirety as well as making legal advice and justice more accessible to all.

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Notes

  1. 1.

    Asimov (1950), p. 189.

  2. 2.

    Weller (2016).

  3. 3.

    Kennedy (2017), p. 170.

  4. 4.

    Kasperkevic (2015), quot. in. Kennedy (2017), p. 172.

  5. 5.

    Kennedy (2017), p. 172.

  6. 6.

    Anderson (2011), p. 294.

  7. 7.

    Farivar (2016) quot. in. Kennedy (2017), p. 172.

  8. 8.

    Cavoukian (2010).

  9. 9.

    Fingas (2017) quot. in. Kennedy (2017), p. 171.

  10. 10.

    See also Mommers et al. (2009).

  11. 11.

    Frankling and Graesser (1997), quot. in. Brożek and Jakubiec (2017), p. 293.

  12. 12.

    Frankling and Graesser (1997), quot. in. Brożek and Jakubiec (2017), p. 294.

  13. 13.

    Lame (2004), p. 382.

  14. 14.

    Bibel (2004), p. 163.

  15. 15.

    See Mommers et al. (2009) p. 52.

  16. 16.

    McGinnis and Wasick (2015), p. 993.

  17. 17.

    McGinnis and Wasick (2015), p. 997.

  18. 18.

    Bibel (2004), p. 164.

  19. 19.

    Mommers et al. (2009), p. 53.

  20. 20.

    Susskind (2017), p. 191.

  21. 21.

    See also Susskind (2017).

  22. 22.

    This principle is applicable in legal search as in any other. See also McGinnis and Wasick (2015), p. 1017.

  23. 23.

    Bouaziz et al. (2018), p. 2.

  24. 24.

    IBM Watson. Available at: https://www.ibm.com/watson/. Accessed 27 April 2018.

  25. 25.

    IBM Systems & Technology Group (2011), p. 5.

  26. 26.

    Mommers et al. (2009), p. 55.

  27. 27.

    See Wettig and Zehendner (2004).

  28. 28.

    See Footnote 13.

  29. 29.

    Lame (2004), p. 395.

  30. 30.

    Baude and Sachs (2017), p. 1085.

  31. 31.

    Baude and Sachs (2017), p. 1083.

  32. 32.

    Husa (2016), p. 263.

  33. 33.

    See Footnote 30.

  34. 34.

    Baude and Sachs (2017), p. 1123.

  35. 35.

    See Footnote 14.

  36. 36.

    See Footnote 14.

  37. 37.

    See Footnote 14.

  38. 38.

    See Footnote 10.

  39. 39.

    See Footnote 26.

  40. 40.

    Baude and Sachs (2017), p. 1088.

  41. 41.

    Husa (2016), p. 270.

  42. 42.

    See Footnote 19.

  43. 43.

    Moore (2010), p. 4.

  44. 44.

    Moore (2010), p. 7.

  45. 45.

    Kac and Ulam (1968), p. 4.

  46. 46.

    Hodel (1995), p. 1.

  47. 47.

    Henket (2003), p. 1.

  48. 48.

    Leith (1988), p. 32.

  49. 49.

    Bibel (2004), p. 176.

  50. 50.

    See Footnote 49.

  51. 51.

    For the sake of the research questions, it is vital to know that this paper uses a generalized view of the discipline of law .

  52. 52.

    Leith (1991), p. 201.

  53. 53.

    Trevor et al. (2006), p. 1.

  54. 54.

    Leith (1988), p. 34.

  55. 55.

    See Footnote 54.

  56. 56.

    Leith (1988), p. 35.

  57. 57.

    Stolpe (2010), p. 247.

  58. 58.

    Lame (2004), p. 380.

  59. 59.

    See Footnote 58.

  60. 60.

    Mommers et al. (2009), p. 72.

  61. 61.

    Mommers et al. (2009), p. 75.

  62. 62.

    Wallach and Allen (2008), p. 23.

  63. 63.

    Wallach and Allen (2008), p. 17.

  64. 64.

    Guarini (2013), p. 213.

  65. 65.

    Wallach and Allen (2008), p. 13.

  66. 66.

    Nyholm and Smids (2016), p. 1288.

  67. 67.

    Nyholm and Smids (2016), p. 1285.

  68. 68.

    Nyholm and Smids (2016), p. 1280.

  69. 69.

    Shulman et al. (2009), p. 1.

  70. 70.

    Anderson and Anderson (2007), p. 1.

  71. 71.

    See also Anderson and Anderson (2007), p. 4.

  72. 72.

    See also Anderson and Anderson (2007), p. 2.

  73. 73.

    Anderson (2011), p. 287.

  74. 74.

    Anderson and Anderson (2007), p. 4.

  75. 75.

    Anderson and Anderson (2007), p. 1. The example used here is one of care-robots being developed for elderly-homes in the United States and emphasis is placed on the need to instill ethical principles in those robots to ensure proper care and safety.

  76. 76.

    Asimov (1950).

  77. 77.

    Clarke (2011), p. 256.

  78. 78.

    Clarke (2011), p. 259.

  79. 79.

    Asimov (1964), introduction; see also Weng et al. (2009).

  80. 80.

    See, e.g., Weng et al. (2009); Anderson (2008), (2011)

  81. 81.

    Clarke (2011), p. 260.

  82. 82.

    Clarke (2011), p. 272.

  83. 83.

    Nunez (2015), p. 204.

  84. 84.

    This is not the case today, where no AI is actually intelligent. See Storrs Hall (2011), p. 512.

  85. 85.

    See Footnote 8.

  86. 86.

    Mackworth (2011), p. 347.

  87. 87.

    Storrs Hall (2011), p. 522.

  88. 88.

    Storrs Hall (2011), p. 523.

  89. 89.

    Asimov (1950), p. 195.

  90. 90.

    Anderson (2011), p. 295; Kant thought that human-to-human behavior can deteriorate if humans are taught to mistreat other species, therefore it is in the interest of humanity that each human treat other species in a fair way. See Anderson (2011), p. 294.

  91. 91.

    See Anderson (2011).

  92. 92.

    See Bibel (2004).

  93. 93.

    See Bibel (2004); Susskind (2017).

  94. 94.

    Ambrogi (2017).

  95. 95.

    See Oskamp and Lauritsen (2002).

  96. 96.

    See also Leith (1988), p. 34.

  97. 97.

    Remus and Levy (2016), p. 9.

  98. 98.

    Henket (2003), p. 131.

  99. 99.

    Oskamp and Lauritsen (2002), p. 232.

  100. 100.

    See Footnote 99.

  101. 101.

    Winick (2017); see also Cellan-Jones (2017).

  102. 102.

    ROSS. Available at: https://rossintelligence.com. Accessed 27 April 2018.

  103. 103.

    Nunez (2017), p. 193.

  104. 104.

    Nunez (2017), p. 194.

  105. 105.

    Oskamp and Lauritsen (2002).

  106. 106.

    See also Henket (2003).

  107. 107.

    Henket (2003), p. 128; Henket beautifully describes and exemplifies ways in which humans will be essential to the development and upkeep of these systems, at least for the foreseeable future.

  108. 108.

    Henket (2003), p. 128.

  109. 109.

    Henket (2003), p. 129.

  110. 110.

    Oskamp et al. (1995).

  111. 111.

    Neural networks are described as “an artificial imitation of their biological model, the brains and nervous systems of humans and animals. The ability to learn and the use of parallelism during data processing are important characteristics.” Wettig and Zehendner (2004).

  112. 112.

    See also Bibel (2004).

  113. 113.

    Henket (2003), p. 136.

  114. 114.

    Leeson (2017), p. 41.

  115. 115.

    See Footnote 114.

  116. 116.

    For a good analysis of the role of evidence with reference to Solomon’s judgment, see LaRue (2004).

  117. 117.

    LaRue (2004).

  118. 118.

    See Footnote 117.

  119. 119.

    See also Clarke (2011), p. 280.

  120. 120.

    See Footnote 119.

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Acknowledgements

My gratitude goes out to Fredrik Persson and Andrej Bracanović for their input and immense patience during my many brainstorming sessions about robot lawyers.

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  1. Stockholm, Sweden

    Dena Dervanović

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  1. Dena Dervanović
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  1. Center for Innovation and Research, Universidad Politécnica y Artística del Paraguay (UPAP), Asunción, Paraguay

    Marcelo Corrales

  2. Faculty of Law, Kyushu University, Fukuoka, Japan

    Mark Fenwick

  3. Department of Innovation and Digitalisation in Law, University of Vienna, Vienna, Austria

    Nikolaus Forgó

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Dervanović, D. (2018). I, Inhuman Lawyer: Developing Artificial Intelligence in the Legal Profession. In: Corrales, M., Fenwick, M., Forgó, N. (eds) Robotics, AI and the Future of Law. Perspectives in Law, Business and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-13-2874-9_9

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