Fair Use in AI Data Training: Judgment Criteria and Balancing Mechanisms

Yaorunyang Ding

doi:10.54254/2753-8818/2025.CH22295

Research Article

Open access

Published on 24 April 2025

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Ding,Y. (2025). Fair Use in AI Data Training: Judgment Criteria and Balancing Mechanisms. Theoretical and Natural Science,101,69-77.

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Fair Use in AI Data Training: Judgment Criteria and Balancing Mechanisms

Yaorunyang Ding *^,1,

¹ Institute of Foreign Languages, Heilongjiang University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2025.CH22295

Abstract

Against the backdrop of the rapid development of artificial intelligence technology, the issue of fair use in AI data training has sparked widespread attention and discussion. Large model training relies on massive amounts of data, whose technical characteristics differ significantly from how traditional works are used. However, market failures are prevalent, with high licensing costs and difficulties in obtaining rights holders' permissions hindering the effective operation of traditional authorization mechanisms. Therefore, the use of works in large-scale model training should be considered as fair use, since it has a limited impact on the legitimate rights of copyright holders while offering significant social and public benefits. In addition, within the framework of copyright law, it is essential to clarify the rules and criteria for fair use in machine learning and to define the obligations and responsibilities of AI data trainers. This will help balance the interests of copyright holders, society, and data trainers, thereby promoting the healthy and sustainable development of AI technology and the adaptive evolution of copyright law.

Keywords

AI data training, Fair use, Interest balancing, Machine learning types

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References

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Cite this article

Ding,Y. (2025). Fair Use in AI Data Training: Judgment Criteria and Balancing Mechanisms. Theoretical and Natural Science,101,69-77.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of CONF-MPCS 2025 Symposium: Mastering Optimization: Strategies for Maximum Efficiency

Conference website: https://www.confmpcs.org/workshop_chicago.html

ISBN：978-1-80590-017-7(Print) / 978-1-80590-018-4(Online)

Conference date: 21 March 2025

Editor：Anil Fernando, Marwan Omar

Series: Theoretical and Natural Science

Volume number: Vol.101

ISSN：2753-8818(Print) / 2753-8826(Online)

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