The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Future
Not on the map. Why?
Abstract (arXiv)
As ChatGPT et al. conquer the world, the optimal liability framework for AI systems remains an unsolved problem across the globe. In a much-anticipated move, the European Commission advanced two proposals outlining the European approach to AI liability in September 2022: a novel AI Liability Directive and a revision of the Product Liability Directive. They constitute the final cornerstone of EU AI regulation. Crucially, the liability proposals and the EU AI Act are inherently intertwined: the latter does not contain any individual rights of affected persons, and the former lack specific, substantive rules on AI development and deployment. Taken together, these acts may well trigger a Brussels Effect in AI regulation, with significant consequences for the US and beyond. This paper makes three novel contributions. First, it examines in detail the Commission proposals and shows that, while making steps in the right direction, they ultimately represent a half-hearted approach: if enacted as foreseen, AI liability in the EU will primarily rest on disclosure of evidence mechanisms and a set of narrowly defined presumptions concerning fault, defectiveness and causality. Hence, second, the article suggests amendments, which are collected in an Annex at the end of the paper. Third, based on an analysis of the key risks AI poses, the final part of the paper maps out a road for the future of AI liability and regulation, in the EU and beyond. This includes: a comprehensive framework for AI liability; provisions to support innovation; an extension to non-discrimination/algorithmic fairness, as well as explainable AI; and sustainability. I propose to jump-start sustainable AI regulation via sustainability impact assessments in the AI Act and sustainable design defects in the liability regime. In this way, the law may help spur not only fair AI and XAI, but potentially also sustainable AI (SAI).
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 19 of its 243 references that are in the map. First posted on arXiv 25 Nov 2022.
Builds on
19 of 243 references.
References · 19
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
- 2021Carbon Emissions and Large Neural Network Trainingcited by 567
- 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 460
- 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 365
- 2021Pre-Trained Models: Past, Present and Futurecited by 319
- 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 165
- 2020Algorithmic Fairnesscited by 143
- 2018Explaining Explanations in AIcited by 137
7 more
- 2021Demystifying the Draft EU Artificial Intelligence Actcited by 84
- 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
- 2022Toward a realistic model of speech processing in the brain with self-supervised learningcited by 63
- 2021Compute and Energy Consumption Trends in Deep Learning Inferencecited by 61
- 2021Human-AI Interactions in Public Sector Decision-Making: "Automation Bias" and "Selective Adherence" to Algorithmic Advicecited by 22
- 2021Gradual (In)Compatibility of Fairness Criteriacited by 8
- 2022Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and lawcited by 2
Built on by
Citing papers per year: 2023: 5; 2024: 4; 2025: 3.
12 papers.
- 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 154
- 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
- 2023Regulating ChatGPT and other Large Generative AI Modelscited by 104
- 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
- 2024Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecuritycited by 21
- 2024AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Biascited by 3
- 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Frameworkcited by 2
- 2025Breaking Down Bias: On The Limits of Generalizable Pruning Strategiescited by 2
- 2023The risks of risk-based AI regulation: taking liability seriouslycited by 1
- 2024Mapping the Potential of Explainable AI for Fairness Along the AI Lifecyclecited by 1
- 2023The Opaque Law of Artificial Intelligencecited by 0
- 2025The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectationscited by 0
- 2025The Engineer's Dilemma: A Review of Establishing a Legal Framework for Integrating Machine Learning in Construction by Navigating Precedents and Industry Expectations11 Jul 2025
- 2025Breaking Down Bias: On The Limits of Generalizable Pruning Strategies11 Feb 2025
- 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework14 Jan 2025
- 2024AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias29 Jul 2024
- 2024Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle29 Apr 2024
- 2024Black-Box Access is Insufficient for Rigorous AI Audits25 Jan 2024
- 2024Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity14 Jan 2024
- 2023The risks of risk-based AI regulation: taking liability seriously3 Nov 2023
- 2023The Opaque Law of Artificial Intelligence19 Oct 2023
- 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives29 Sep 2023
- 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models4 Apr 2023
- 2023Regulating ChatGPT and other Large Generative AI Models5 Feb 2023