CoChat
Sign Up
Cited by 15Explainable Artificial Intelligence (XAI)Digitalization, Law, and RegulationArtificial Intelligence in Law

AI Opacity and Explainability in Tort Litigation

Open access
Fraser, Henry, Simcock, Rhyle, Snoswell, Aaron J.·2022·2022 ACM Conference on Fairness Accountability and Transparency
DOI 10.1145/3531146.3533084
Open this paper in CoChat — free accountOpen PDF

Abstract

A spate of recent accidents and a lawsuit involving Tesla's ‘self-driving’ cars highlights the growing need for meaningful accountability when harms are caused by AI systems. Tort (or civil liability) lawsuits are one important way for victims to redress such harms. The prospect of tort liability may also prompt AI developers to take better precautions against safety risks. Tort claims of all kinds will be hindered by AI opacity: the difficulty of determining how and why complex AI systems make decisions. We address this problem by formulating and evaluating several options for mitigating AI opacity that combine expert evidence, legal argumentation, civil procedure, and Explainable AI approaches. We emphasise the need for explanations of AI systems in tort litigation to be attuned to the elements of legal ‘causes of action’ – the specific facts that must be proven to succeed in a lawsuit. We take a recent Australian case involving explainable AI evidence as a starting point from which to map contemporary Explainable AI approaches to elements of tortious causes of action, focusing on misleading conduct, negligence, and product liability for safety defects. Our work synthesizes law, legal procedure, and computer science to provide greater clarity on the opportunities and challenges for Explainable AI in civil litigation, and may prove helpful to potential litigants, to courts, and to illuminate key targets for regulatory intervention.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3531146.3533084

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 46

  1. Abdul, Ashraf et al, 'Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda' in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (2018) 1
  2. Adadi, Amina and Mohammed Berrada, 'Peeking inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)' (2018) 6 IEEE access 52138
  3. Akbari, Ali et al, 'A Theoretical Insight Into the Effect of Loss Function for Deep Semantic-Preserving Learning' [2021] IEEE Transactions on Neural Networks and Learning Systems 1
  4. Atiyah, PS, 'Res Ipsa Loquitur in England and Australia' (1972) 35(4) The Modern Law Review 337
  5. Barker, Kit, The Law of Torts in Australia (Oxford University Press, 5th ed, 2012)
  6. Bloch-Wehba, Hannah, 'Access to Algorithms' (2019) 88(4) Fordham Law Review 1265
  7. Brauneis, Robert and Ellen P Goodman, 'Algorithmic Transparency for the Smart City' (2018) 20 Yale Journal of Law and Technology 103
  8. Cane, Peter and James Goudkamp, Atiyah's Accidents, Compen- sation and the Law (Cambridge University Press, 9 th ed, 2018) <https://www.cambridge.org/core/books/atiyahs-accidents-compensation- and-the-law/47E05F02588747B1B0AA27FD9F732391>
  9. Cauffman, Caroline, 'Robo-Liability: The European Union in Search of the Best Way to Deal with Liability for Damage Caused by Artificial Intelligence' (2018) 25(5) Maastricht Journal of European and Comparative Law
  10. Chinen, Mark A, 'The Co-Evolution of Autonomous Machines and Legal Re- sponsibility' (2016) 20 Vanderbilt Journal of Law and Technology 338
  11. Citron, Danielle Keats, 'Technological Due Process' (2007) 85(6) Washington University Law Review 1249
  12. Deeks, Ashley, 'The Judicial Demand for Explainable Artificial Intelligence' (2019) 119(7) Columbia Law Review 1829
  13. Desai, Deven R and Joshua A Kroll, 'Trust but Verify: A Guide to Algorithms and the Law' (2017) 31(1) Harvard Journal of Law & Technology 1
  14. Doshi-Velez, Finale and Been Kim, 'Towards A Rigorous Science of Interpretable Machine Learning' [2017] arXiv:1702.08608 [cs, stat] <http://arxiv.org/abs/1702. 08608> · arXiv
  15. Fleming, John G, 'Probabilistic Causation in Tort Law' (1989) 68(4) The Canadian Bar Review 661
  16. Fraser, Henry L and Jose-Miguel Bello y Villarino, 'Where Residual Risks Reside: A Comparative Approach to Art 9(4) of the European Union's Proposed AI Regulation' [2021] Working Draft <https://papers.ssrn.com/abstract=3960461>
  17. Goldenfein, Jake et al, 'Through the Handoff Lens: Competing Visions of Au- tonomous Futures' (2020) 35(4) Berkeley Technology Law Journal 835
  18. Graham, Kyle, 'Of Frightened Horses and Autonomous Vehicles: Tort Law and Its Assimilation of Innovations' (2012) 52(4) Santa Clara Law Review 1241
  19. Gurumoorthy, Karthik S, Amit Dhurandhar and Guillermo Cecchi, 'Protodash: Fast Interpretable Prototype Selection' [2017] arXiv preprint arXiv:1707.01212 · arXiv
  20. Hayward, Benjamin, 'What's in a Name?: Software, Digital Products, and the Sale of Goods' (2016) 38(4) Sydney Law Review 441
  21. Henderson, James A, 'Judicial Review of Manufacturers' Conscious Design Choices: The Limits of Adjudication' (1973) 73(8) Columbia Law Review 1531
  22. Howells, Geraint, Christian Twigg-Flesner and Chris Willett, 'Product Liability and Digital Products' in Tatiana-Eleni Synodinou et al (eds), EU Internet Law: Regulation and Enforcement (Springer International Publishing, 2017) 183 <https: //doi.org/10.1007/978-3-319-64955-9_8> · DOI
  23. Huberman, Pinchas, 'Tort Law, Corrective Justice and the Problem of Autonomous-Machine-Caused Harm' (2021) 34(1) Canadian Journal of Law & Jurisprudence 105
  24. Josephson, John R and Susan G Josephson, Abductive Inference: Computation, Philosophy, Technology (Cambridge University press, 1996) <https://www.google.com.au/books/edition/Abductive_Inference/ uu6zXrogwWAC?hl=en&gbpv=1&pg=PA1&printsec=frontcover>
  25. Katyal, Sonia K, 'Private Accountability in the Age of Artificial Intelligence' (2019) 66(1) UCLA Law Review 54
  26. Kim, Been, Rajiv Khanna and Oluwasanmi O Koyejo, 'Examples Are Not Enough, Learn to Criticize! Criticism for Interpretability' (2016) 29 Advances in neural information processing systems
  27. Krafft, Tobias D, Katharina A Zweig and Pascal D König, 'How to Regulate Algorithmic Decision-Making: A Framework of Regulatory Requirements for Different Applications' Regulation & Governance <http://onlinelibrary.wiley. com/doi/abs/10.1111/rego.12369> · DOI · Paper page
  28. Lemer, Bruce, 'Strict Products Liability: The Problem of Improperly Designed Products' (1982) 20(2) Osgoode Hall Law Journal 250
  29. Lemley, Mark A and Bryan Casey, 'Remedies for Robots' (2019) 86(5) The Uni- versity of Chicago Law Review 1311
  30. Lindsay, David F, Evana Wright and Genevieve Wilkinson, Regulating to Protect Security & Privacy in the Internet of Things (IoT): Draft Report (SSRN Scholarly Paper No 4052068, Social Science Research Network, 11 February 2022) <https: //papers.ssrn.com/abstract=4052068>
  31. Liu, Han-Wei, Ching-Fu Lin and Yu-Jie Chen, 'Beyond State v Loomis: Artificial Intelligence, Government Algorithmization and Accountability' (2019) 27(2) International Journal of Law and Information Technology 122
  32. Loren, Lydia Pallas and Andy Johnson-Laird, 'Computer Software-Related Liti- gation: Discovery and the Overly-Protective Order' (2012) 6 Lewis and Clark Law Review 75
  33. Lundberg, Scott M and Su-In Lee, 'A Unified Approach to Interpreting Model Pre- dictions' in Proceedings of the 31st International Conference on Neural Information Processing Systems (2017) 4768
  34. Masnadi-Shirazi, Hamed and Nuno Vasconcelos, 'On the Design of Loss Func- tions for Classification: Theory, Robustness to Outliers, and SavageBoost' in Proceedings of the 21st International Conference on Neural Information Processing Systems (2008) 1049
  35. Rachum-Twaig, Omri, 'Whose Robot Is It Anyway?: Liability for Artificial- Intelligence-Based Robots' (2020) 2020(4) University of Illinois Law Review 1141
  36. Ribeiro, Marco Tulio, Sameer Singh and Carlos Guestrin, "'Why Should i Trust You?" Explaining the Predictions of Any Classifier' in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016) 1135
  37. Saito, Sean and Sujoy Roy, 'Effects of Loss Functions and Target Representations on Adversarial Robustness' [2018] arXiv preprint arXiv:1812.00181 · arXiv
  38. Sambasivan, Nithya et al, 'Everyone Wants to Do the Model Work, Not the Data Work: Data Cascades in High-Stakes AI' in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Association for Computing Machinery, 2021) 1 <https://doi.org/10.1145/3411764.3445518> · DOI · Paper page
  39. Selbst, Andrew, 'An Institutional View Of Algorithmic Impact Assessments' 35(Forthcoming) Harvard Journal of Law & Technology
  40. Selbst, Andrew D and Solon Barocas, 'The Intuitive Appeal of Explainable Machines' (2018) 87(3) Fordham Law Review 1085
  41. Smart, William D, Cindy Grimm and Woodrow Hartzog, 'An Education Theory of Fault For Autonomous Systems' (2021) 2 Notre Dame Journal on Emerging Technologies 33
  42. Stapleton, Jane, 'Compensating Victims of Diseases' (1985) 5 Oxford J. Legal Stud. 248
  43. Vladeck, David C, 'Machines Without Principals: Liability Rules and Artificial Intelligence' (2014) 89 Washington Law Review 117
  44. Wachter, Sandra, Brent Mittelstadt and Chris Russell, 'Counterfactual Expla- nations Without Opening the Black Box: Automated Decisions and the GDPR' (2018) 31 Harvard Journal of Law & Technology 841
  45. White, Adam and Artur d'Avila Garcez, 'Measurable Counterfactual Local Ex- planations for Any Classifier' [2019] arXiv preprint arXiv:1908.03020 · arXiv
  46. Yoshikawa, Jin, 'Sharing the Costs of Artificial Intelligence: Universal No-Fault Social Insurance for Personal Injuries' (2018) 21(4) Vanderbilt Journal of Enter- tainment and Technology Law 1155

Cited by

Sorted by citations

Authors
Fraser, H., Simcock, R., & Snoswell, A. J.
Affiliations
Queensland University of Technology (AU)
ORCID
Venue
2022 ACM Conference on Fairness Accountability and Transparency
Publisher
ACM
Published
2022-06-20
Pages
185-196
DOI
10.1145/3531146.3533084
OpenAlex
W4283170465
Cited by
15
References
9
Citations by year
2026: 6 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 2
FWCI
1.24
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
  • QUT ePrints (Queensland University of Technology) (submittedVersion)
Funders
Australian Research Council Centre of Excellence for Automated Decision-Making and Society (CE200100005)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
Keywords
AI opacity, explainable AI, tort litigation, civil liability, cause of action, expert testimony, negligence, product liability, autonomous vehicles, Tesla, AI accountability, civil procedure, legal argumentation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review