Copyright Policy Options for Generative Artificial Intelligence

28 Pages Posted: 16 Feb 2024 Last revised: 12 Apr 2024

See all articles by Joshua S. Gans

Joshua S. Gans

University of Toronto - Rotman School of Management; NBER

Date Written: April 11, 2024

Abstract

New generative artificial intelligence (AI) models, including large language models and image generators, have created new challenges for copyright policy as such models may be trained on data that includes copy-protected content. This paper examines this issue from an economic perspective and analyses how different copyright regimes for generative AI will impact the quality of content generated as well as the quality of AI training. A key factor is whether generative AI models are small (with content providers capable of negotiations with AI providers) or large (where negotiations are prohibitive). For small AI models, it is found that giving original content providers copyright protection leads to superior social welfare outcomes compared to having no copyright protection. For large AI models, this comparison is ambiguous and depends on the level of potential harm to original content providers and the importance of content for AI training quality. However, it is demonstrated that an ex-post `fair use' type mechanism can lead to higher expected social welfare than traditional copyright regimes.

Keywords: artificial intelligence, copyright, fair use, generative AI, large language models

JEL Classification: K2, O34

Suggested Citation

Gans, Joshua S., Copyright Policy Options for Generative Artificial Intelligence (April 11, 2024). Available at SSRN: https://ssrn.com/abstract=4707911 or http://dx.doi.org/10.2139/ssrn.4707911

Joshua S. Gans (Contact Author)

University of Toronto - Rotman School of Management ( email )

Canada

HOME PAGE: http://www.joshuagans.com

NBER ( email )

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Cambridge, MA 02138
United States

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