Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models

Aloni Cohen

Keywords: copyright, generative models, law, definitions, AI and law, differential privacy

TL;DR: This paper revisits the question of provable copyright protection for generative models: identifying limitations of prior work, proposing new definitions, and describing conditions under which differential privacy suffices.

Abstract: Are there any conditions under which a generative model’s outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak [ICML 2023]. They define _near access-freeness (NAF)_ and propose it as sufficient for protection. This paper revisits the question and establishes new foundations for provable copyright protection---foundations that are firmer both technically and legally. First, we show that NAF alone does not prevent infringement. In fact, NAF models can enable verbatim copying, a blatant failure of copy protection that we dub being _tainted_. Then, we introduce our _blameless copy protection framework_ for defining meaningful guarantees, and instantiate it with _clean-room copy protection_. Clean-room copy protection allows a user to control their risk of copying by behaving in a way that is unlikely to copy in a counterfactual "clean-room setting." Finally, we formalize a common intuition about differential privacy and copyright by proving that DP implies clean-room copy protection when the dataset is _golden_, a copyright deduplication requirement.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 26453