I have a new draft essay, The Files are in the Computer: On Copyright, Memorization, and Generative AI. It is a joint work with my regular co-author A. Feder Cooper, who just completed his Ph.D. in Computer Science at Cornell. We presented an earlier version of the paper at the online AI Disrupting Law Symposium symposium hosted by the Chicago-Kent Law Review in April, and the final version will come out in the CKLR. Here is the abstract:
The New York Times’s copyright lawsuit against OpenAI and Microsoft alleges that OpenAI’s GPT models have “memorized” Times articles. Other lawsuits make similar claims. But parties, courts, and scholars disagree on what memorization is, whether it is taking place, and what its copyright implications are. Unfortunately, these debates are clouded by deep ambiguities over the nature of “memorization,” leading participants to talk past one another.
In this Essay, we attempt to bring clarity to the conversation over memorization and its relationship to copyright law. Memorization is a highly active area of research in machine learning, and we draw on that literature to pro- vide a firm technical foundation for legal discussions. The core of the Essay is a precise definition of memorization for a legal audience. We say that a model has “memorized” a piece of training data when (1) it is possible to reconstruct from the model (2) a near-exact copy of (3) a substantial portion of (4) that specific piece of training data. We distinguish memorization from “extraction” (in which a user intentionally causes a model to generate a near-exact copy), from “regurgitation” (in which a model generates a near-exact copy, regardless of the user’s intentions), and from “reconstruction” (in which the near-exact copy can be obtained from the model by any means, not necessarily the ordinary generation process).
Several important consequences follow from these definitions. First, not all learning is memorization: much of what generative-AI models do involves generalizing from large amounts of training data, not just memorizing individual pieces of it. Second, memorization occurs when a model is trained; it is not something that happens when a model generates a regurgitated output. Regurgitation is a symptom of memorization in the model, not its cause. Third, when a model has memorized training data, the model is a “copy” of that training data in the sense used by copyright law. Fourth, a model is not like a VCR or other general-purpose copying technology; it is better at generating some types of outputs (possibly including regurgitated ones) than others. Fifth, memorization is not just a phenomenon that is caused by “adversarial” users bent on extraction; it is a capability that is latent in the model itself. Sixth, the amount of training data that a model memorizes is a consequence of choices made in the training process; different decisions about what data to train on and how to train on it can affect what the model memorizes. Seventh, system design choices also matter at generation time. Whether or not a model that has memorized training data actually regurgitates that data depends on the design of the overall system: developers can use other guardrails to prevent extraction and regurgitation. In a very real sense, memorized training data is in the model–to quote Zoolander, the files are in the computer.