Liability Regimes in the Age of AI: a Use-Case Driven Analysis of the Burden of Proof
Open accessAbstract
New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concern about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, may lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies. This article appears in the AI & Society track.
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.
Integrity signals
Registry
Cites
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.
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?
Current liability frameworks are insufficient for addressing causation issues related to AI, especially regarding the distribution of the burden of proof across jurisdictions.
“Current liability frameworks are insufficient for addressing causation issues related to AI, especially regarding the distribution of the burden of proof across jurisdictions (Llorca et al., 2023).”
From Causation in AI Tort Litigation: Legal Dilemmas of Algorithmic Black Boxes and Burden of Proof Allocation (2025) · cites this paper as Llorca et al., 2023
The Conclusions state that liability regimes should be revised to alleviate victims’ burden of proof in AI cases, and Related Work describes the difficulty of proving causation. The paper discusses how proof burdens differ between liability regimes, but the cited passages and a search for “jurisdiction” do not support the claim about their distribution across jurisdictions. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
The Limitations of Deep Learning in Adversarial Settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2016.36 · Cited by 3,993 · see citing works
How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1× · see citing works
“Opacity arises from the inability to provide human-scale reasoning from complex AI models (Burrell, 2016).”
BackgroundAn Overview of Overfitting and its Solutions
Ying, X. (2019) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/1168/2/022022 · Cited by 2,352 · see citing works
Catastrophic forgetting in connectionist networks
French, R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01294-2 · Cited by 2,311 · see citing works
Can we open the black box of AI?
Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773 · see citing works
Socially intelligent robots: Dimensions of human-robot interaction
Dautenhahn, K. (2007) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2006.2004 · Cited by 1,210 · see citing works
Toward Causal Representation Learning
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170 · see citing works
The Curse of Dimensionality in Data Mining and Time Series Prediction
Verleysen, M., & François, D. (2005) · Lecture notes in computer science · doi:10.1007/11494669_93 · Cited by 746 · see citing works
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Mandic, D. P., & Chambers, J. A. (2001) · ORCA Online Research @Cardiff (Cardiff University) · Cited by 640 · see citing works
The Intuitive Appeal of Explainable Machines
Selbst, A. D., & Barocas, S. (2018) · doi:10.2139/ssrn.3126971 · Cited by 392 · see citing works
A Survey of Learning Causality with Data: Problems and Methods
Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2021) · ACM Computing Surveys · doi:10.1145/3397269 · Cited by 362 · see citing works
Hacker, P. (2018) · Common Market Law Review · doi:10.54648/cola2018095 · Cited by 317 · see citing works
Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302 · see citing works
(2018) · Harvard journal of law & technology · Cited by 295 · see citing works
(2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 291 · see citing works
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
Cheng, L., Varshney, K. R., & Liu, H. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12814 · Cited by 261 · see citing works
Towards Intelligent Regulation of Artificial Intelligence
Buiten, M. C. (2019) · European Journal of Risk Regulation · doi:10.1017/err.2019.8 · Cited by 242 · see citing works
Liability for damages caused by artificial intelligence
Čerka, P., Grigienė, J., & Sirbikytė, G. (2015) · Computer law & security review · doi:10.1016/j.clsr.2015.03.008 · Cited by 217 · see citing works
Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI?
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.160 · Cited by 188 · see citing works
AI WATCH. Defining Artificial Intelligence
Samoili, S., Montserrat, L. C., Emilia, G. G., Giuditta, D. P., Martínez‐Plumed, F., & Blagoj, D. (2020) · Joint Research Centre (European Commission) · doi:10.2760/382730 · Cited by 154 · see citing works
(2014) · Washington law review · Cited by 147 · see citing works
Peters, J., Janzing, D., & Schölkopf, B. (2012) · Cited by 130 · see citing works
Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI
Holzinger, A., & Müller, H. (2021) · Computer · doi:10.1109/mc.2021.3092610 · Cited by 95 · see citing works
Cabitza, F., Campagner, A., Malgieri, G., Natali, C., Schneeberger, D., Stoeger, K., & Holzinger, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118888 · Cited by 89 · see citing works
Zhang, Y., Song, W., Tan, Z., Zhu, H., Wang, Y., Lam, C. M., Weng, Y., Hoi, S. P., Lu, H., Chan, B. S. M., et al. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.04.008 · Cited by 82 · see citing works
Taking Principles Seriously: A Hybrid Approach to Value Alignment in Artificial Intelligence
Kim, T. W., Hooker, J. F., & Donaldson, T. J. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12481 · Cited by 57 · see citing works
Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model
Saarela, M., & Geogieva, L. (2022) · Applied Sciences · doi:10.3390/app12199545 · Cited by 55 · see citing works
Liability for AI Decision-Making: Some Legal and Ethical ConsiderationsPreprint
Giuffrida, I. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4953916 · Cited by 44 · see citing works
Fair and Unbiased Algorithmic Decision Making: Current State and Future ChallengesPreprint
Tolan, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.04730 · Cited by 43 · see citing works
Charisi, V., Gómez, E., Mier, G., Merino, L., & Gómez, R. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00015 · Cited by 43 · see citing works
Self-citationPunishing Artificial Intelligence: Legal Fiction or Science Fiction
Abbott, R. B., Abbott, R. B., Sarch, A., & Sarch, A. (2020) · Hart Publishing eBooks · doi:10.5040/9781509937097.ch-008 · Cited by 37 · see citing works
Socially intelligent robots: dimensions of human–robot interaction
Dautenhahn, K. (2009) · doi:10.1093/oso/9780199216543.003.0014 · Cited by 36 · see citing works
Gerards, J. H., Xenidis, R., handhaving, R. /. R. E., recht, E., van de mens, R., Rechtspleging, M. C. V. R. E., rechten, F., Böök, B., Senden, L. A. J., Timmer, A., et al. (2021) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 36 · see citing works
Black is the new orange: how to determine AI liability
Padovan, P. H., Martins, C. M., & Reed, C. (2022) · Artificial Intelligence and Law · doi:10.1007/s10506-022-09308-9 · Cited by 35 · see citing works
Socially Responsible AI Algorithms: Issues, Purposes, and ChallengesPreprint
Cheng, L., Varshney, K. R., & Liu, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02032 · Cited by 34 · see citing works
Fosch‐Villaronga, E., & Drukarch, H. (2022) · CRC Press eBooks · doi:10.1201/9781003201779 · Cited by 32 · see citing works
Whose Robot Is It Anyway?: Liability for Artificial-Intelligence-Based RobotsPreprint
Rachum-Twaig, O. (2019) · SSRN Electronic Journal · Cited by 26 · see citing works
The AI Liability Puzzle and A Fund-Based Work-Around
Erdélyi, O. J., & Erdélyi, G. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375806 · Cited by 25 · see citing works
Basic Questions of Tort Law from a Comparative Perspective
Askeland, B., Yamamoto, K., Oliphant, K., Moréteau, O., Menyhárd, A., Ludwichowska-Redo, K., Koziol, H., Green, M. D., & Cardi, W. J. (2015) · doi:10.26530/oapen_574832 · Cited by 23 · see citing works
Comparative Law Study on Civil Liability for Artificial Intelligence
Geistfeld, M., Karner, E., & Koch, B. A. (2022) · De Gruyter eBooks · doi:10.1515/9783110775402-001 · Cited by 23 · see citing works
Punishing Artificial Intelligence: Legal Fiction or Science Fiction
Abbott, R. B., & Sarch, A. (2024) · Data science, machine intelligence, and law · doi:10.1007/978-3-031-47946-5_6 · Cited by 17 · see citing works
Safety- and Liability-Related Aspects of Software
Wendehorst, C. C., & Duller, Y. (2022) · De Gruyter eBooks · doi:10.1515/9783110775402-002 · Cited by 16 · see citing works
AI Opacity and Explainability in Tort Litigation
Fraser, H., Simcock, R., & Snoswell, A. J. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533084 · Cited by 15 · see citing works
Transparency and Fairness in Machine Learning Applications
Shook, J., Smith, R., & Antonio, A. (2018) · doi:10.37419/jpl.v4.i5.2 · Cited by 10 · see citing works
Artificial Intelligence and Damages: Assessing Liability and Calculating Damages
Benhamou, Y., & Ferland, J. (2021) · Archive ouverte UNIGE (University of Geneva) · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 47 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Pearl, J. (2009). Causality (2 edition). Cambridge University Press.
Cited by
Sorted by citations
Williamson, S. M., & Prybutok, V. (2024) · Information · doi:10.3390/info15060299 · Cited by 146 · see citing works
Menéses, E. L., Catalán, L. L., Piris, N. P., & Mellado-Moreno, P. C. (2025) · Applied Sciences · doi:10.3390/app15020772 · Cited by 56 · see citing works
Llorca, D. F., Hamon, R., Junklewitz, H., Grosse, K., Kunze, L., Seiniger, P., Swaim, R., Reed, N., Alahi, A., Gómez, E., et al. (2025) · European Transport Research Review · doi:10.1186/s12544-025-00732-x · Cited by 46 · see citing works
Self-citationPredictable artificial intelligence
Zhou, L., Casares, P. A. M., Martínez‐Plumed, F., Burden, J., Burnell, R., Cheke, L. G., Ferri, C. S., Marcoci, A., Mehrbakhsh, B., Moros-Daval, Y., et al. (2026) · Artificial Intelligence · doi:10.1016/j.artint.2026.104491 · Cited by 18 · see citing works
Ethical Governance of Artificial Intelligence Hallucinations in Legal Practice
Warraich, M. K. S., Usman, H., Zakir, S., & Mehboob, M. (2025) · Social Sciences Spectrum · doi:10.71085/sss.04.02.297 · Cited by 5 · see citing works
Artificial Intelligence Systems and Non-Contractual Civil Liability: A Risk-Based Approach
Аликович, И. О., & Kuteynikov, D. L. (2024) · Lex Russica · doi:10.17803/1729-5920.2024.211.6.023-034 · Cited by 3 · see citing works
Yu, N., Chen, L., & Yi, X. (2024) · Displays · doi:10.1016/j.displa.2024.102933 · Cited by 2 · see citing works
Pryor, K., Coleman, T., Hossain, S. Z., Tootil, E., & Ahmed, S. (2025) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac65507.2025.00115 · Cited by 2 · see citing works
Kazantsev, D. A. (2025) · Journal of Digital Technologies and Law · doi:10.21202/jdtl.2025.18 · Cited by 2 · see citing works
Ma, J. (2025) · Legal Compliance · doi:10.63808/lc.v1i2.127 · Cited by 1 · see citing works
“Current liability frameworks are insufficient for addressing causation issues related to AI, especially regarding the distribution of the burden of proof across jurisdictions (Llorca et al., 2023).”
Yu, N., chen, L., Yi, X., & Huang, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105215 · Cited by 1 · see citing works
Aldmour, R. (2025) · Journal of Lifestyle and SDGs Review · doi:10.47172/2965-730x.sdgsreview.v5.n05.pe06197 · Cited by 1 · see citing works
AI Risk Bonds: a market-based mechanism for governing liability
Papyshev, G., Chan, K. J. D., & Migliorini, S. (2026) · Data & Policy · doi:10.1017/dap.2026.10079 · Cited by 1 · see citing works
Rismawati, R., Hasan, F., Widiasmara, A., Nur, M., & Navghare, R. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5951-9.ch008
Howard, R., Hawes, N., & Kunze, L. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127745
YANG, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5574199
The Criminal Liability Of Hinotori: The Japanese Surgical Robotic Doctor
Bal, E. N., & Abualrob, M. (2026) · Journal of Penal Law and Criminology / Ceza Hukuku ve Kriminoloji Dergisi · doi:10.26650/jplc2026-1711852
Examining the AI’s legal status from legal and theoretical perspectives
Labanieh, M. F., Yusoff, Z. M., Ayub, Z. A., Wahab, H. A., & Shariffuddin, M. D. K. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2026779
AI Accountability and Liability
Ogedengbe, F. A., & Adelowotan, M. O. (2026) · CSR, sustainability, ethics & governance · doi:10.1007/978-3-032-20091-4_4
Ser, J. D., Moreno-Sánchez, P. A., Gómez, E., & Llorca, D. F. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104861
Self-citationLiability for rogue behaviors of agentic AI
Papyshev, G., Chan, K. J. D., & Migliorini, S. (2026) · Telecommunications Policy · doi:10.1016/j.telpol.2026.103339
- Authors
- Fernández Llorca, D., Charisi, V., Hamon, R., Sánchez, I., & Gómez, E.
- Affiliations
- European Commission (BE)
- Universidad de Alcalá (ES)
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2023-03-12
- Volume
- 76
- Pages
- 613-644
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W4324029514
- Cited by
- 20
- References
- 47
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 3
- FWCI
- 35.80
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Artificial Intelligence Research (publishedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission
- Joint Research Centre
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- AI liability, burden of proof, causation (law), machine learning, compensation for damages, cleaning robots, delivery drones, educational robots, algorithmic opacity, self-learning AI, victim protection
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
