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
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
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.