Decisions · Policy · United States

Fair Learning

Mark A. Lemley and Bryan Casey (Stanford Law School) · 2021 · Proposal · non-binding

Texas Law Review, vol. 99 (2021)

AI training and copyright

Can AI be trained on protected works?

Freely allowed

Using copyrighted works to train machine learning should generally be fair use

Why hereCoded at the pole: its core holding or recommendation answers the question outright.

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.