Private Law Theory

ABSTRACT
Generative AI systems often produce false and defamatory statements about real individuals, but traditional defamation law falters when the speaker lacks a mind. New York Times Co v Sullivan requires proving actual malice – knowledge of falsity or reckless disregard for truth – but algorithms have no ability to know about falsity or act recklessly. To address the gap, this research proposes a doctrinal framework – redistributed malice – that maintains Sullivan’s constitutional protections by shifting fault inquiries from algorithmic outputs to associated human actors. Drawing on Eugene Volokh’s notice-based liability, Nina Brown’s product-liability concept, and traditional defamation doctrine, the framework allocates responsibilities this way: developers face heightened liability after receiving notice of specific false outputs; deployers face context-based negligence standards; end-users face traditional actual malice adapted to AI systems’ hallucination risks. This three-tier structure maintains the public figure and private figure distinction and demonstrates that actual malice’s constitutional function survives technological evolution, through systemic reallocation, instead of doctrinal abandonment.

Haque, Misbahul, Defamation by AI: Actual Malice, Artificial Intelligence, and the Future of Fault (December 31, 2025), AEJMC 109th Annual Conference, Law and Policy Division.