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Artificial Moral Responsibility: How We Can and Cannot Hold Machines Responsible

Published online by Cambridge University Press:  10 June 2021

Daniel W. Tigard
Daniel W. Tigard*
Affiliation:
Institute for History and Ethics of Medicine, Technical University of Munich, Ismaninger Str. 22 81675Munich, Germany
*
*Corresponding author. Email: [email protected]
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Abstract

Our ability to locate moral responsibility is often thought to be a necessary condition for conducting morally permissible medical practice, engaging in a just war, and other high-stakes endeavors. Yet, with increasing reliance upon artificially intelligent systems, we may be facing a widening responsibility gap, which, some argue, cannot be bridged by traditional concepts of responsibility. How then, if at all, can we make use of crucial emerging technologies? According to Colin Allen and Wendell Wallach, the advent of so-called ‘artificial moral agents’ (AMAs) is inevitable. Still, this notion may seem to push back the problem, leaving those who have an interest in developing autonomous technology with a dilemma. We may need to scale-back our efforts at deploying AMAs (or at least maintain human oversight); otherwise, we must rapidly and drastically update our moral and legal norms in a way that ensures responsibility for potentially avoidable harms. This paper invokes contemporary accounts of responsibility in order to show how artificially intelligent systems might be held responsible. Although many theorists are concerned enough to develop artificial conceptions of agency or to exploit our present inability to regulate valuable innovations, the proposal here highlights the importance of—and outlines a plausible foundation for—a workable notion of artificial moral responsibility.

Keywords

moral responsibilitymoral agencyblamemachine ethicsAI ethicsartificial intelligencehuman-robot interaction
Type
Articles
Information
Cambridge Quarterly of Healthcare Ethics , Volume 30 , Issue 3 , July 2021 , pp. 435 - 447
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Notes

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19. In Asimov’s “A Boy’s Best Friend,” for example, the child of a family settled on a future lunar colony cares more for his robotic canine companion than for a real-life dog. Thanks to Nathan Emmerich for the pointer.

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23. Ibid., at 25–26. See also Nyholm S. Humans and Robots: Ethics, Agency, and Anthropomorphism. Rowman & Littlefield; 2020.

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35. See note 5 for similar arguments in healthcare, education, and transportation.

36. See note 4, Sparrow 2007, at 65.

37. Ibid., at 66; italics added.

38. Ibid., at 69; italics added. Comparable inconsistencies are seen in Floridi and Sanders 2004 (note 24).

39. Ibid.

40. Ibid., at 71; italics added.

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45. See note 4, Sparrow 2007, at 71.

46. Ibid., at 72.

47. Ibid.

48. Consider also that we punish corporations (e.g. by imposing fines) despite the implausibility of such entities displaying the right sort of response, an anonymous reviewer aptly notes. By contrast, consequential accounts of punishment can be seen as inadequate depictions of moral blame, since they don’t fully explain our attitudes and might not properly distinguish wrongdoers from others. See Wallace, RJ. Responsibility and the Moral Sentiments. Harvard University Press 1994; 52–62.Google Scholar I’m grateful to Sven Nyholm for discussion here.

49. Proponents of the ‘process view’ applied to technology can be said to include Johnson, DG, Miller, KW. Un-making artificial moral agents. Ethics and Information Technology 2008;10:123–133.CrossRefGoogle Scholar Despite some similarities to this work, my account does not fit neatly into Johnson and Miller’s Computational Modelers or Computers-in-Society group.

50. See Watson, G. Agency and Answerability. Oxford: Oxford University Press 2004; 260–288.CrossRefGoogle Scholar

51. See note note 50, Watson 2004, at 274.

52. See note 10, Shoemaker 2015, at 57.

53. Exemptions are contrasted with excuses (and justifications). See, e.g., Watson 2004;224–225 (note 50).

54. See note 10, Shoemaker 2015, at 146–182.

55. However, these sorts of sanctioning mechanisms are less likely to succeed where the target AI system has surpassed humans in general intelligence. See the discussion of ‘incentive methods’ for controlling AI, in Bostrom, N. Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press 2014: 160–163.Google Scholar

56. Such ‘bottom-up’ moral development in AI is discussed in Allen and Wallach 2009 (note 6). Compare also Hellström, T. On the moral responsibility of military robots. Ethics and Information Technology 2013;15:99–107CrossRefGoogle Scholar. Again, for some (e.g. Wallace 1994, in note 48), consequential accounts of responsibility will be unsatisfying. While a fuller discussion isn’t possible here, in short, my goal has been to unearth general mechanisms for holding diverse objects responsible, which admittedly will deviate from the robust sorts of responsibility (and justifications) we ascribe to natural moral agents. Again, I’m here indebted to Sven Nyholm.

57. See, e.g., Ren, F. Affective information processing and recognizing human emotion. Electronic Notes in Theoretical Computer Science 2009;225:39–50CrossRefGoogle Scholar. Consider also recent work on Amazon’s Alexa, e.g., in Knight W. Amazon working on making Alexa recognize your emotions. MIT Technology Review 2016.

58. Similarly, Helen Nissenbaum suggests that although accountability is often undermined by computing, we can and should restore it, namely by promoting an ‘explicit standard of care’ and imposing ‘strict liability and producer responsibility.’ Nissenbaum, H. Computing and accountability. Communications of the ACM 1994;37:72–80CrossRefGoogle Scholar; Nissenbaum, H. Accountability in a computerized society. Science and Engineering Ethics 1996;2:25–42.CrossRefGoogle Scholar I thank an anonymous reviewer for connecting my account with Nissenbaum’s early work.

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61. John Danaher likewise frames the problem in terms of trade-offs, namely increases in efficiency and perhaps well-being, but at the cost of human participation and comprehension. See Danaher, J. The threat of algocracy: reality, resistance and accommodation. Philosophy and Technology 2016;29:245–268CrossRefGoogle Scholar; also Robots, Danaher J, law and the retribution gap. Ethics and Information Technology 2016;18:299–309.Google Scholar

62. In a follow-up paper, I explain further how pluralistic conceptions of responsibility can address the alleged gap created by emerging technologies. See Tigard D. There is no techno-responsibility gap. Philosophy and Technology 2020; available at: https://doi.org/10.1007/s13347-020-00414-7.