Copy, Paste, and Generate: Copyright Law and Fair Use in the Age of Artificial Intelligence

Abstract

This article examines whether the U.S. doctrine of fair use can adequately address the legal and ethical challenges posed by the training of generative artificial intelligence (GenAI) systems. The key research question driving this analysis is: Can fair use, as currently interpreted, provide a coherent and equitable framework for addressing the massive, automated ingestion of copyrighted works for the AI model development? This research is guided by two hypotheses. First, the transformative-use doctrine, while important in modern fair use analysis, remains insufficiently defined and inconsistent to address the functional and non-expressive nature of AI training. Second, that the current litigation landscape, as seen in The New York Times v. Microsoft Corporation and OpenAI, et al. (case no. 1:23-cv-11195 Southern District of New York) "OpenAI case" and Dow Jones & Company, Inc. et al v. Perplexity AI, Inc. (case no. 1:24-cv-07984 Southern District of New York) "Perplexity case", indicates an urgent need for legislative clarification to harmonize innovation incentives with copyright protection in the age of machine learning. The article uses a doctrinal legal analysis, examining case law, statutes, and policies, to show how fair use law has changed and how it applies to new AI technology. This methodological approach contextualizes the dispute within its historical origins and current policy ramifications, offering a cohesive legal and ethical framework for evaluating the limits of fair use in the age of generative AI.