Copyright in Generative Deep Learning
| Autor: | Franceschelli, Giorgio, Musolesi, Mirco |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Data & Policy. 2022;4:e17 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1017/dap.2022.10 |
| Popis: | 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. Comment: 16 pages. Second version contains updates after entry into force of EU's directive on copyright in the Digital Single Market, and corrections of typos. Third version contains a new section about GitHub Copilot and its copyright implications. Fourth version contains improvements in abstract, introduction and conclusions, and a general rearrangement of the central sections |
| Databáze: | arXiv |
| Externí odkaz: |
|