A University of Florida analysis argues that US courts are quietly shaping AI governance through case-by-case decisions rather than broad legislation, signaling a shift toward experience-based rulemaking over upfront design.

The common-law approach offers gradual adaptation and flexibility, but it also brings slow, costly litigation and a patchwork of precedents that can create legal uncertainty around AI.

Key areas of impact include employment discrimination rulings that hold employers liable for AI hiring tools and defamation cases assessing how AI-generated statements are interpreted, including the role of disclaimers or context.

Courts are integrating AI into existing legal doctrines by focusing on human actors and institutions rather than the AI itself.

Geographic concentration in California and New York means a small set of courts wield outsized influence over AI rulings.

Copyright disputes stand among the most consequential, with cases like Andersen v. Stability AI and The New York Times Co. v. OpenAI testing whether training AI on copyrighted works requires permission and shaping future incentives for creativity.

The current environment features limited enacted AI legislation at state and federal levels, pushing courts to rely on common-law reasoning as Congress has yet to act.

Garcia v. Character Technologies signals products-liability considerations for AI, with a court allowing negligence claims against a chatbot developer.

Overall, American AI governance is increasingly shaped by judicial decisions that evolve with technology, reflecting a preference for experiential rulemaking over prescriptive legislation.

Compared with Europe’s legislative approach, the US path may avoid premature rigid rules but risks lagging in clarity and uniformity across jurisdictions.