Decisions · Policy · United States

Foundation models and fair use

Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley and Percy Liang (Stanford University) · 2023 · Proposal · non-binding

SSRN 4404340 (2023); Journal of Machine Learning Research 24 (2023)

Liability for AI outputs

Is an AI provider liable when outputs copy protected works?

Liable in some cases

Fair use is not guaranteed when outputs resemble training data; pair the law with technical safeguards

Why hereLiable in some cases: fair use may not cover outputs similar to training data, especially where they affect the work's market; safe harbours could reward strong mitigations.

What it says

Training on copyrighted material may be fair use, but if a model produces output similar to its training data, especially where it affects that work's market, fair use may not apply to the output. Experiments show popular models can produce substantially similar content. The authors propose technical mitigations and suggest the law could offer safe harbours where strong mitigations are used.

A law and computer science view: liability turns on outputs, and safeguards should earn protection.

Sources

This record

Data as of 2026-10-21.

Drafted with AI assistance and checked against the sources linked above; not yet reviewed by a lawyer. Methodology

No changes logged yet. The change log started on 2026-10-20.