Copyright in Generative Deep Learning

• URL: http://arxiv.org/abs/2105.09266v1
• Date: Wed, 19 May 2021 17:22:47 GMT
• Title: Copyright in Generative Deep Learning
• Authors: Giorgio Franceschelli and Mirco Musolesi
• Abstract summary: We consider a set of key questions in the area of generative deep learning for the arts. We try to answer these questions considering the law in force in both US and EU.
• Score: 3.689181056530984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Also given their success, several legal problems arise when working with these techniques. In this article we consider a set of key questions in the area of generative deep learning for the arts. 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? And then, who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both US and EU and the future alternatives, trying to define a set of guidelines for artists and developers working on deep learning generated art.

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