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A Typology of Liability Rules for Robot Harms

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A World with Robots

Part of the book series: Intelligent Systems, Control and Automation: Science and Engineering ((ISCA,volume 84))

Abstract

This paper considers non-contractual liability for harms caused by (artificially) intelligent systems. It provides a typology of different ways to approach the liability issue, exemplified by some new technologies that have been, or are about to be, introduced into human society. The paper argues that the traditional robot-as-tool perspective should be maintained, but warns that this might not be possible unless we develop corresponding technologies for efficient responsibility tracking. Specifically, new techniques need to be developed, at the intersection between computer science and law, to support reasoning about the liability implications when autonomous technologies interact with their environment and cause harms.

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Notes

  1. 1.

    See, generally, Cerka et al. (2015); Hallevy (2015); Vladeck (2014); Allain (2013); Gurney (2013).

  2. 2.

    See (Palmerini et al. 2014, 175).

  3. 3.

    See, generally, Howells and Owen (2010).

  4. 4.

    See Howells and Owen (2010, 241).

  5. 5.

    The benefit of using robots to perform surgery are obvious; robot hands are steady and precise, more so than the hands of human surgeons. In addition, the da Vinci system makes it possible for human doctors to perform surgery from afar, so they can help people without being physically present. The system has proved successful, and so far it has been deployed to around 2500 hospitals around the world. See Kirkpatrick (2014, 14).

  6. 6.

    Apparently, by October 2012 around 3000 claims had been submitted, see Moylan (2014).

  7. 7.

    Mracek v Bryn Mawr Hospital, 610 F Supp 2d 401 (ED Pa 2009), aff’d, 363 F App’x 925 (3d Cir 2010).

  8. 8.

    For a more in-depth discussion of the case, see Pagallo (2013, 91–95) and Goldberg (2012).

  9. 9.

    This is particularly likely to become a problem when, as in the case of da Vinci, the manufacturer is a de facto knowledge monopolist, with access to privileged information about how the system actually works. See Goldberg (2012, 249) (“Because Intuitive has a monopoly, as well as limited expert witnesses, manipulation of an industry standard by such a dominant player is likely in a situation where they would be protecting themselves in a products liability lawsuit.”). This also points to the important interactions between liability rules and intellectual property rights, a connection that could provide a strong incentive for large technology firms to favour an approach based on insurance payments and new forms of strict liability. In this way, these firms might hope to avoid in-depth public scrutiny of their technology, both its merits and its shortcomings.

  10. 10.

    Following this, the collective as such can be held liable, either proportionally to their causal contribution or else in solidium (joint liability). See, e.g., Wright (1992).

  11. 11.

    See, e.g., Hamdani and Klement (2008).

  12. 12.

    See generally Allain (2013).

  13. 13.

    Allain (2013, 1077).

  14. 14.

    Allain (2013, 1075).

  15. 15.

    Allain refers to his status as being that of a “quasi-legal person”. See Allain (2013).

  16. 16.

    This should be contrasted with doctrines of strict liability, where other notions act as a substitute for culpa, e.g., the notion of a defect in products liability law. See, e.g., Calabresi and Hirschoff (1972, 1055–1056). From a conceptual and socio-legal perspective, it should also be kept in mind that causation itself might be used as a partly normative notion, see, generally, Hitchcock and Knobe (2009).

  17. 17.

    See, generally, Vladeck (2014).

  18. 18.

    Vladeck (2014, 146).

  19. 19.

    Vladeck (2014, 149).

  20. 20.

    Vladeck argues that this is close to the legal doctrine of res ipsa loquitur (going as far as to state that it is merely a “restatement” of it). See Vladeck (2014, 128). I do not agree. According to Vladeck’s proposal, liability will be inferred even in the complete absence of circumstantial/statistical evidence to suggest a design flaw—in fact, it might be inferred even in the presence of conclusive evidence suggesting that the autonomous car is a safer driver than most humans. The proposal therefore appears very different from the original doctrine of res ipsa loquitur, which is a principle used to infer negligence from circumstantial evidence. See, generally, Johnson (1997).

  21. 21.

    Vladeck suggests that a vicarious liability system should be built on this basis, so that human actors can be held accountable for the agency of the cars even if they are entirely blameless. Specifically, he proposes an enterprise model involving not only the manufacturer but also the designers of the AI software and other technology suppliers, see Vladeck (2014, 148–149).

  22. 22.

    Vladeck (2014, 145–149).

  23. 23.

    Vladeck (2014, 150).

  24. 24.

    See, e.g., Chopra and White (2011, 189–191); Koops et al. (2010, 560–561) (“The majority view in the literature is that sooner or later, limited legal personhood with strict liability is a good solution for solving the accountability gap, particularly in contracting, and for electronic agents, this may be sooner rather than later”). For a bolder approach, arguing that notions of criminal liability should apply to artificial intelligences, see Hallevy (2013, 177–178).

  25. 25.

    See, generally, Rosen (2011).

  26. 26.

    See, e.g., Bachmann (2013).

  27. 27.

    See Strawser (2010); Arkin (2010).

  28. 28.

    See Arkin (2010).

  29. 29.

    See, generally, Sharkey (2010).

  30. 30.

    See generally Keller (2012).

  31. 31.

    Keller (2012).

  32. 32.

    Keller (2012).

  33. 33.

    See Keller (2012).

  34. 34.

    Two famous examples are the Italy 2003 electricity outage that left virtually all of Italy without electricity, and the 2011 Southwest blackout, which affected more than 7 million people in the US. See, generally, Buldyrev et al. (2010).

  35. 35.

    See Goodley (2015). In my opinion, this approach to liability for the flash crash is highly problematic, particularly as the damage created by cascades will tend to be completely out of proportion to any mistakes or even bad intentions that originally set them in motion.

  36. 36.

    See Smith (2014).

  37. 37.

    See Hope (2015).

  38. 38.

    The REINS project at Utrecht University, with which I am affiliated, aims to make a contribution in this regard, see Broersen (2014).

  39. 39.

    There is interesting work being carried out in this direction, but it remains rather embryonic, see, e.g., Dennis et al. (2015).

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Authors and Affiliations

  1. Department of Philosophy and Religious Studies, Utrecht University, Utrecht, The Netherlands

    Sjur Dyrkolbotn

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  1. Sjur Dyrkolbotn
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Correspondence to Sjur Dyrkolbotn .

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Editors and Affiliations

  1. Center of Philosophy, University of Lisbon Center of Philosophy, Lisbon, Portugal

    Maria Isabel Aldinhas Ferreira

  2. Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal

    Joao Silva Sequeira

  3. Automatic Control & Systems Engineering, The University of Sheffield, Sheffield, United Kingdom

    Mohammad Osman Tokhi

  4. Department of Psychology, University of Portsmouth, Portsmouth, United Kingdom

    Endre E. Kadar

  5. Faculty of Engineering, University of Gävle, Gävle, Sweden

    Gurvinder Singh Virk

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Dyrkolbotn, S. (2017). A Typology of Liability Rules for Robot Harms. In: Aldinhas Ferreira, M., Silva Sequeira, J., Tokhi, M., E. Kadar, E., Virk, G. (eds) A World with Robots. Intelligent Systems, Control and Automation: Science and Engineering, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-319-46667-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-46667-5_9

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