Ketan Ramakrishnan (Yale University, Law School) has posted Tort Law at the Frontier of Artificial Intelligence, Yale Journal on Regulation (forthcoming 2026), on SSRN. Here is the abstract:
The frontier of contemporary AI development is dominated by AI systems built on foundation models – highly versatile algorithms, trained in the first instance on broad swathes of data, that can function as tools and agents across a wide variety of commercial, social, military and political domains. For the moment, at least, the process of developing and releasing foundation models is subject to anemic formal regulation and haphazard ex ante governance. Until that changes, it is largely the common law of torts – our society’s most ancient and general legal mechanism for governing serious risks of physical injury – that will govern the frontier of AI development.
This Article offers an in-depth conceptual, normative, and doctrinal examination of tort liability for foundation model development and release. It provides a qualified defense of the tort of negligence – the common law’s broadest and most flexible cause of action – as the principal doctrinal foundation of the tort system’s governance of this novel domain. Legal scholarship on AI liability has been quite hostile to negligence. By contrast, this Article argues that the generality and flexibility of the negligence tort – and its greater sensitivity to the externalized benefits of risky activity – render it well-suited to the polymathic and protean functionality of foundation models. Only the tort of negligence has the breadth and flexibility to address the range of important pathways – including internal deployments, inadequate model weight security, targeted entrustments of non-defective models, and open source releases – by which foundation model developers might cause serious harm.
Analyzing the choice between negligence and competing doctrinal regimes does, however, suggest important ways in which common law courts should incrementally develop the law of negligence, in order to properly reflect the risks and capabilities of foundation models. For example, courts should expand the scope of the duty of care in negligence, in order to provide redress when foundation models cause economic or emotional injury by behaving in ways that would violate important human laws or norms of behavior (e.g., certain crimes and intentional torts, such as theft, deceit, and outrage) if those models were human. Similarly, courts should recognize a malfunction doctrine in the law of negligence (just as many courts recognize a malfunction doctrine in products liability), under which a plaintiff can get to the jury without adducing (further) evidence of breach if she is injured by AI system that so behaves.
But the Article’s analysis also suggests certain fundamental pathologies of tort liability as a mechanism of AI governance – pathologies that no amount of doctrinal development will adequately cure. In particular, the specter of tort liability can be expected to disincentivize frontier AI developers from investigating and disclosing many of the novel and poorly understood risks that frontier AI development may pose. That is especially disturbing given that our society is relying quite heavily, for its ability to discover and understand these risks, on frontier AI developers themselves. Thus, tort liability is not only inadequate as a mechanism of frontier AI governance; in certain important respects, it is actively perverse, and its perverse effects must be countered by governance institutions of a different kind. Ultimately, a robust regime of ex ante regulation – under which government institutions or credibly neutral third-party experts are empowered to investigate, evaluate, and mitigate the risks of frontier AI development – is urgently required in frontier AI governance.
Highly recommended!
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