We Should Separate Memorization from Copyright
This addresses copyright concerns in foundation models for data scientists and legal scholars, offering a framework to clarify infringement risks, though it is incremental in refining existing debates.
The paper argues that current technical methods conflate memorization with copyright infringement, proposing a distinction between lawful generalization and actual copying risks. It advocates for an output-level, risk-based evaluation process to align technical assessments with copyright standards.
The widespread use of foundation models has introduced a new risk factor of copyright issue. This issue is leading to an active, lively and on-going debate amongst the data-science community as well as amongst legal scholars. Where claims and results across both sides are often interpreted in different ways and leading to different implications. Our position is that much of the technical literature relies on traditional reconstruction techniques that are not designed for copyright analysis. As a result, memorization and copying have been conflated across both technical and legal communities and in multiple contexts. We argue that memorization, as commonly studied in data science, should not be equated with copying and should not be used as a proxy for copyright infringement. We distinguish technical signals that meaningfully indicate infringement risk from those that instead reflect lawful generalization or high-frequency content. Based on this analysis, we advocate for an output-level, risk-based evaluation process that aligns technical assessments with established copyright standards and provides a more principled foundation for research, auditing, and policy.