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Black is the new orange: how to determine AI liability

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Abstract

Autonomous artificial intelligence (AI) systems can lead to unpredictable behavior causing loss or damage to individuals. Intricate questions must be resolved to establish how courts determine liability. Until recently, understanding the inner workings of “black boxes” has been exceedingly difficult; however, the use of Explainable Artificial Intelligence (XAI) would help simplify the complex problems that can occur with autonomous AI systems. In this context, this article seeks to provide technical explanations that can be given by XAI, and to show how suitable explanations for liability can be reached in court. It provides an analysis of whether existing liability frameworks, in both civil and common law tort systems, with the support of XAI, can address legal concerns related to AI. Lastly, it claims their further development and adoption should allow AI liability cases to be decided under current legal and regulatory rules until new liability regimes for AI are enacted.

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Notes

  1. See this discussion in the report mentioned above, House of Lords (2018), and in Robot Law Calo et al. (2016, pp. introduction xiv/ xv, 98).

  2. For Wright (1985), this test means “something is a cause if it is a ‘necessary element of a set of conditions jointly sufficient for the result.”

  3. The only exception to strict liability that does not demand a ‘causation’ element in Brazilian law is related to integral risk theory.

  4. See Bloch (2005).

  5. Ibid.

  6. See Bloch (2011).

  7. See Muschara (2007).

  8. See Cohen (1995).

  9. See Angelov and Soares (2019).

  10. See Ribeiro et al. (2016).

  11. See Lundberg and Lee (2017).

  12. See Friedman (2001).

  13. See Ho (1995).

  14. See Goldstein et al. (2015).

  15. See Friedman (2001).

  16. See Gu et al. (2019) and Nicolae et al. (2018).

  17. See Verma and J Rubin (2018), d’Alessandro et al. (2017), and Friedler et al. (2016).

  18. See (Piatetsky-Shapiro 2007).

  19. See (Harper and Pickett 2006).

  20. See Chapman et al. (2000).

  21. See Aïvodji et al. (2019).

  22. See Berendt and Preibusch (2012) and Adebayo (2016).

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Acknowledgements

We gratefully acknowledge Dr Armando Castro’s invaluable comments on the revision of this article.

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Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco UFPE, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária (Campus Recife), Recife, PE, Brasil, 50740-560

    Paulo Henrique Padovan

  2. Communication Department, Catholic University of Pernambuco (UNICAP), Rua do Príncipe, 526, Boa Vista, Recife, PE, Brasil, 50050-900

    Clarice Marinho Martins

  3. Visiting Scholar 2019/2020 at the Centre for Commercial Law Studies, Queen Mary University of London, London, UK

    Clarice Marinho Martins

  4. Centre for Commercial Law Studies, Queen Mary University of London, 67-69 Lincoln’s Inn Fields, London, WC2A 3JB, England, UK

    Chris Reed

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  1. Paulo Henrique Padovan
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Correspondence to Clarice Marinho Martins.

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Padovan, P.H., Martins, C.M. & Reed, C. Black is the new orange: how to determine AI liability. Artif Intell Law 31, 133–167 (2023). https://doi.org/10.1007/s10506-022-09308-9

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