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Assessing the feasibility of collective licensing of in-copyright works as training data for generative AI systems.

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Bibliographic Details
Title:	Assessing the feasibility of collective licensing of in-copyright works as training data for generative AI systems.
Authors:	Samuelson, Pamela¹ psamuelson@berkeley.edu
Source:	Proceedings of the National Academy of Sciences of the United States of America. 7/28/2026, Vol. 123 Issue 30, p1-10. 10p.
Subjects:	Generative artificial intelligence, License agreements, Royalties (Copyright), Legal liability, Actions & defenses (Law), Copyright infringement
Abstract:	Copyright owners have sued several developers of l arge-s cale generative AI systems for copyright infringement because of their uses of massive quantities of in-c opyright works as training data for building AI models. Fair use will be the main defense against these charges. If fair use defenses succeed, developers will be free to continue to commercially exploit models already built on copyrighted data as well as to use these data to train new models or fine-t une existing ones. If copyright owners prevail, developers may be liable for billions of dollars of damages. Developers could also be enjoined from further model development on in-copyright works and even ordered to destroy models trained on infringing works. Numerous commentators have proposed collective licensing as a compromise solution to the copyright-trainingdata dilemma. Other commentators have questioned the feasibility of such a compromise. This article discusses several proposals for collective licensing to enable development of generative AI systems while providing some compensation to copyright owners. It assesses the complex normative, economic, and practical problems that must be addressed if such a regime is to become feasible. It discusses the implications of a licensing mandate not only for the large firms whose models are widely used today, but also for startups, research centers, and higher education developers of generative AI systems, as well as the general public. [ABSTRACT FROM AUTHOR]
	Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Copyright owners have sued several developers of l arge-s cale generative AI systems for copyright infringement because of their uses of massive quantities of in-c opyright works as training data for building AI models. Fair use will be the main defense against these charges. If fair use defenses succeed, developers will be free to continue to commercially exploit models already built on copyrighted data as well as to use these data to train new models or fine-t une existing ones. If copyright owners prevail, developers may be liable for billions of dollars of damages. Developers could also be enjoined from further model development on in-copyright works and even ordered to destroy models trained on infringing works. Numerous commentators have proposed collective licensing as a compromise solution to the copyright-trainingdata dilemma. Other commentators have questioned the feasibility of such a compromise. This article discusses several proposals for collective licensing to enable development of generative AI systems while providing some compensation to copyright owners. It assesses the complex normative, economic, and practical problems that must be addressed if such a regime is to become feasible. It discusses the implications of a licensing mandate not only for the large firms whose models are widely used today, but also for startups, research centers, and higher education developers of generative AI systems, as well as the general public. [ABSTRACT FROM AUTHOR]
ISSN:	00278424
DOI:	10.1073/pnas.2509769122