Decisions · Policy · United States
Fair Learning
Mark A. Lemley and Bryan Casey (Stanford Law School) · 2021 · Proposal · non-binding
Can AI be trained on protected works?
Freely allowed
Using copyrighted works to train machine learning should generally be fair use
What it says
Arguing that courts may wrongly treat non-transformative, whole-dataset copying as free riding, the authors propose 'fair learning': using works to train machine learning systems, which learn unprotectable patterns rather than expression, should generally be treated as fair use.
The influential US case for letting AI learn from works without permission.
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.