Developing Artificial Intelligence-Based Content Creation: Are EU Copyright and Antitrust Law Fit for Purpose?

Author:

Vesala JuhaORCID

Abstract

AbstractArtificial intelligence offers promising applications for content production. However, their development faces significant copyright issues because it involves reproduction of protected subject matter and requires datasets so large that obtaining licences from all rightholders is unfeasible. These issues potentially hinder technological development and content production. On the other hand, some AI applications can threaten the interests and incentives of those who create works and subject matter that are protected by related rights. This article examines whether EU copyright and antitrust law are capable of addressing these challenges. It identifies possibilities and obstacles in applying exceptions for text and data mining (TDM) and temporary copying to the development of artificial creativity (AC) applications. The article also examines mechanisms by which EU antitrust law facilitates access to copyright-protected training materials and licences – an important complement to the copyright exceptions. While copyright and antitrust law enable the development of AC in certain situations, their tools are limited to particular types of AI applications, certain categories of subject matter and specific market conditions, and are subject to requirements concerning the development process as well as considerable legal uncertainty. Copyright and antitrust law also remain largely toothless against contractual and technological restraints, while recent EU initiatives dealing with data access also provide little relief in this regard.

Funder

University of Lapland

Publisher

Springer Science and Business Media LLC

Subject

Law,Political Science and International Relations

Reference49 articles.

1. Aggarwal C (2015) Data mining the textbook. https://doi.org/10.1007/978-3-319-14142-8

2. Brown TB et al. (2020) Language models are few-shot learners. arXiv:2005.14165 [cs.CL]. Accessed 30 Nov 2022

3. Chiou T (2020) Copyright lessons on machine learning: what impact on algorithmic art? 10 (2020) JIPITEC 398 para. 1. https://nbn-resolving.org/urn:nbn:de:0009-29-50250. Accessed 30 Nov 2022

4. Crémer J et al. (2019) Competition policy for the digital era. https://doi.org/10.2763/407537

5. Drexl J (2017) Designing competitive markets for industrial data—between propertisation and access. JIPITEC 8:257 para. 1 https://nbn-resolving.org/urn:nbn:de:0009-29-46365. Accessed 30 Nov 2022

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