Philipp Hacker (European University Viadrina Frankfurt (Oder) – European New School of Digital Studies) has posted “The European AI Liability Directives – Critique of a Half-Hearted Approach and Lessons for the Future” on SSRN. Here is the abstract:
The optimal liability framework for AI systems remains an unsolved problem across the globe. With ChatGPT and other large models taking the technology to the next level, solutions are urgently needed. 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 (AILD) and a revision of the Product Liability Directive (PLD). They constitute the final cornerstone of AI regulation in the EU. Crucially, the liability proposals and the 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 other countries.
Against this background, 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 makes suggestions for amendments to the proposed AI liability framework. They are collected in a concise Annex at the end of the paper. I argue, inter alia, that the dichotomy between the fault-based AILD Proposal and the supposedly strict liability PLD Proposal is fictional and should be abandoned; that an EU framework for AI liability should comprise one fully harmonizing regulation instead of two insufficiently coordinated directives; and that the current proposals unjustifiably collapse fundamental distinctions between social and individual risk by equating high-risk AI systems in the AI Act with those under the liability framework.
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. More specifically, I make four key proposals. Effective compensation should be ensured by combining truly strict liability for certain high-risk AI systems with general presumptions of defectiveness, fault and causality in cases involving SMEs or non-high-risk AI systems. The paper introduces a novel distinction between illegitimate- and legitimate-harm models to delineate strict liability’s scope. Truly strict liability should be reserved for high-risk AI systems that, from a social perspective, should not cause harm (illegitimate-harm models, e.g., autonomous vehicles or medical AI). Models meant to cause some unavoidable harm by ranking and rejecting individuals (legitimate-harm models, e.g., credit scoring or insurance scoring) may only face rebuttable presumptions of defectiveness and causality. General-purpose AI systems should only be subjected to high-risk regulation, including liability for high-risk AI systems, in specific high-risk use cases for which they are deployed. Consumers ought to be liable based on regular fault, in general.
Furthermore, innovation and legal certainty should be fostered through a comprehensive regime of safe harbours, defined quantitatively to the best extent possible. Moreover, trustworthy AI remains an important goal for AI regulation. Hence, the liability framework must specifically extend to non-discrimination cases and provide for clear rules concerning explainability (XAI).
Finally, awareness for the climate effects of AI, and digital technology more broadly, is rapidly growing in computer science. In diametrical opposition to this shift in discourse and understanding, however, EU legislators thoroughly neglect environmental sustainability in both the AI Act and the proposed liability regime. To counter this, 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).