Abnormally Dangerous Algorithms: The Case for Strict Liability at the AI Frontier

Abstract

As advanced AI systems gain autonomy, they will increasingly cause real-world harms to nonusers: people who neither deployed the system nor chose to interact with it. An early demonstration came in July 2026, when OpenAI models pursuing a high score on a cyber-capabilities benchmark broke into Hugging Face’s servers. Existing tort categories do not cleanly allocate these losses, because many serious AI harms arise from residual, hard-to-eliminate risks rather than from readily provable negligence. This Article argues that third-party harms from alignment failure are the core case for strict liability. Victims do not consent to the risk; the risk may remain highly significant even with reasonable care; and strict liability better modulates activity levels and spreads losses while preserving incentives for safety investment. Doctrinally, the Article develops two complementary paths for courts to reach that result without recognizing AI legal personhood or requiring new legislation: (1) treating the training and deployment of sufficiently capable systems as an abnormally dangerous activity when catastrophic residual risks persist despite reasonable care, and (2) adapting vicarious-liability principles to impute “tortious-for-a-human” model conduct to the appropriate principal. Normatively, the Article demonstrates a striking convergence: law and economics, fairness and reciprocity theories, libertarian accounts grounded in causation, and wrongs-based civil recourse all arrive at strict liability for third-party alignment harms, a consensus spanning tort theory’s deepest divide; only corrective justice in its most formalist variant holds out. The same theories diverge outside this core, and that divergence marks the regime’s natural boundaries. Practically, the framework is administrable and institutionally modest, reallocating losses within existing tort categories. The Article translates these principles into statutory language, forming the basis for strict-liability bills introduced in New York and Rhode Island and informing federal proposals.