Copyright in Generative Deep Learning

TL;DR

Copyright in Generative Deep Learning analyzes how US and EU law currently apply to training data usage, model storage, and authorship for AI-generated art and code. It contrasts fair use with text-and-data mining exemptions and evaluates the implications of learned representations and generated outputs for potential infringement or transformation. The paper provides practical guidelines for artists, developers, and policymakers and argues for clearer IP regimes as generative technologies advance. Overall, it highlights the need for balanced legal frameworks that incentivize innovation while protecting rights and enabling verification of results.

Abstract

Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning techniques, which have seen a formidable development and remarkable refinement in the very recent years. Given the inherent characteristics of these techniques, a series of novel legal problems arise. In this article, we consider a set of key questions in the area of generative deep learning for the arts, including the following: is it possible to use copyrighted works as training set for generative models? How do we legally store their copies in order to perform the training process? Who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both the United States of America and the European Union, and potential future alternatives. We then extend our analysis to code generation, which is an emerging area of generative deep learning. Finally, we also formulate a set of practical guidelines for artists and developers working on deep learning generated art, as well as some policy suggestions for policymakers.