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Artificial intelligence and the model of rules: better than us?

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Abstract

Artificial intelligence (AI) decision-making systems are already being extensively used to make decisions in situations where legal rules are applied to establish rights and obligations. In the United States, algorithmic systems are employed to determine the rights of individuals to disability benefits, to evaluate the performance of employees, selecting who will be fired, and to assist judges in granting or denying bail and probation. In this paper I explore some possible implications of H. L. A. Hart’s theory of law as a system of primary and secondary rules to the ongoing debate on the viability and the limits of an adjudicating artificial intelligence. Although much has been recently discussed about the potential practical roles of artificial intelligence in legal practice and assisted decision making, the implication to general jurisprudence still requires further development. I try to map some issues of general jurisprudence that may be consequential to the question of whether a non-human entity (an artificial intelligence) would be theoretically able to perform the kind of legal reasoning made by human judges.

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Notes

  1. See [5].

  2. See [2].

  3. Jeremy WALDRON acknowledges this paradigmatic status of the model of rules when challenging on of its key elements: “The rule of recognition is … a central component of modern positivist jurisprudence.” [18].

  4. See [11].

  5. See [7].

  6. See [15].

  7. Id. at 39–40.

  8. Id. at 41.

  9. Id. at 42.

  10. See [10].

  11. Id. at 602.

  12. Id. at 605.

  13. HART. supra note 3, at 70.

  14. Id. at 81.

  15. Id. at 94.

  16. Even when the addressees of rules are non-human or collective entities, like unions, states, corporations or intelligent machines, rules still ultimately determine acts to be performed or avoided by human beings.

  17. HART. supra note 3, at 81.

  18. Id. at 94.

  19. Id. at 95.

  20. Id. at 97.

  21. DWORKIN. supra note 4, at 35.

  22. HART. supra note 3, at 96.

  23. Id. at 96.

  24. Id.

  25. Id. at 97.

  26. See [3].

  27. See [12].

  28. HART. supra note 3, at 98.

  29. Id. at 10.

  30. Id. at 33.

  31. Id. at 95.

  32. Id. at 101.

  33. See [13].

  34. See [6].

  35. HART. supra note 3, at 205.

  36. Id.

  37. Id.

  38. HILDEBRANDT. supra note 26, at 23.

  39. Id.

  40. See [20].

  41. See [1].

  42. See [16].

  43. Id. at 161.

  44. Id.

  45. See [9].

  46. HILDEBRANDT. supra note 26 at 19.

  47. See [17].

  48. HART. supra note 3, at 261.

  49. Id. at 35–7.

  50. Id. at 36.

  51. Id. at 40.

  52. Id.

  53. See [4].

  54. Id at 53.

  55. Consistent with this picture of legal rules, legal institutions can in turn be described as consisting of “informational processes” organized around exchanges of legal information. See ABITEBOUL & DOWEK. supra note 33, at 82.

  56. ABITEBOUL & DOWEK. supra note 33, at 36.

  57. HART. supra note 3, at 27–8.

  58. BERWICK & CHOMSKY. supra note 52 at 53.

  59. See [14].

  60. Here, I try a parallelism with David Hilbert’s formalist program in mathematics.

  61. HART discusses the essential incompleteness of legal rules when making his case about the problems of the penumbra. See supra note 9, at 612.

  62. RAZ. supra note 5, at 45.

  63. HART. supra note 3, at 79.

  64. Contrary to Hans Kelsen, who insists that norms are always originated from acts of will.

  65. RAZ. supra note 5, at 48.

  66. Assuming that the adjudicating AI in not supplied with, nor it articulates, moral, ideological, political or other standards beyond those assimilated and encoded into the legal corpus.

  67. ABITEBOUL & DOWEK. supra note 33, at 36.

  68. Id. at 2.

  69. See [19].

  70. HART. supra note 3, at 135.

  71. See [8].

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  1. New York University School of Law, New York, NY, USA

    Rodrigo L. Canalli

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Canalli, R.L. Artificial intelligence and the model of rules: better than us?. AI Ethics 3, 879–885 (2023). https://doi.org/10.1007/s43681-022-00210-3

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