Copyright Exceptions and Limitations for AI Innovation: A Need for Tailored Reform?

DOI

10.2991/978-2-38476-547-8_2How to use a DOI?

Keywords

Artificial intelligence; Copyright law; Text and data mining; Fair use; Generative AI; AI training; Copyright exceptions; Innovation policy; Computational analysis; Licensing

Abstract

The intersection of copyright law and artificial intelligence training presents a defining challenge for global innovation policy. Training large-scale generative AI models requires ingesting thousands of copyrighted works, rendering traditional licensing and rights-clearance frameworks economically infeasible. This chapter examines how major jurisdictions have responded to the question of whether and how copyright law should accommodate computational uses of protected works for AI training purposes. Through comparative analysis, it identifies three divergent regulatory approaches: the flexible but unpredictable fair use doctrine employed in the United States, statutory text and data mining (TDM) exceptions with opt-out mechanisms adopted by the European Union, and Japan’s permissive framework allowing non-expressive uses without prior authorization. These jurisdictional variations function not merely as technical differences but as competitive tools that shape AI investment flows and innovation trajectories. Existing frameworks, conceptualized for different technological contexts, fit AI training poorly. No international consensus has emerged regarding fair compensation mechanisms, transparency requirements, or whether current exceptions require adaptation or wholesale replacement. This chapter proposes a tailored reform framework designed specifically for AI training contexts, addressing the idea-expression distinction, global AI competition dynamics, and the emerging “race to the middle” among copyright reformers.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ankit Shrivastava
AU  - Aaratrika Pandey
AU  - Hartej Singh Kochher
PY  - 2026
DA  - 2026/03/05
TI  - Copyright Exceptions and Limitations for AI Innovation: A Need for Tailored Reform?
BT  - Proceedings of the International Conference on Socio Legal Intricacies of Artificial Intelligence (ICSLIAI 2026)
PB  - Atlantis Press
SP  - 4
EP  - 11
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-547-8_2
DO  - 10.2991/978-2-38476-547-8_2
ID  - Shrivastava2026
ER  -