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19 Wash. J. L. Tech. & Arts 1 (2024)

handle is hein.journals/washjolta19 and id is 1 raw text is: WASHINGTON JOURNAL OF LAW, TECHNOLOGY & ARTS VOLUME 19, ISSUE 1 - WINTER 2024 LIMITS OF ALGORITHMIC FAIR USE Jacob Alhadeff', Cooper Cuene 2, and Max Del Real 3 ABSTRACT In this article, we apply historical copyright principles to the evolving state of text-to- image generation and explore the implications of emerging technological constructs for copyright's fair use doctrine. Artificial intelligence (Al) is frequently trained on copyrighted works, which usually involves extensive copying without owners' authorization. Such copying could constitute prima facie copyright infringement, but existing guidance suggests fair use should apply to most machine learning contexts. Mark Lemley and Bryan Casey argue that training machine learning (ML) models on copyrighted material should generally be permitted under fair use when the model's outputs transcends the purpose of its inputs. Their arguments are compelling in the domain of Al, generally. However, contemporary AI's capacity to generate new works of art (generative Al) presents a unique case because it explicitly attempts to emulate the expression copyright intends to protect. Jessica Gillotte concludes that generative Al does not illicit copyright infringement because judicial guidance requires adherence to the constitutional imperative to promote the creation of new works when technological change blurs copyright's boundaries. Even if infringement does occur, Gillotte finds that fair use would serve as a valid defense because training an Al model transforms the original work and is unlikely to damage the original artist's market for the copyrighted work. Our paper deviates from prior scholarship by exploring specific generative Al use cases in technological detail. Ultimately, we argue that fair use's first factor, the purpose of the use, and its fourth factor, the impact on the market for the copyrighted work, both weigh against a finding of fair use in generative Al use cases. However, even if text-to-image models aren't found to be transformative, we argue that the potential for market usurpation alone sufficiently negates fair use. There is presently little specific guidance from courts as to whether using copyrighted works to build generative Al models constitutes either infringement or fair use, although several related lawsuits are currently pending. Text-to-art generative AIs present several scenarios that threaten substantial harm to the market for the copyrighted original, which tends to undercut the case for fair use. For example, a generative Al trained on copyrighted works has already enabled users to create works in the style of' individual artists, which has allegedly caused business and reputational losses for the emulated copyright holder. Furthermore, past analyses have ignored the potential for a model to be non-transformative when its intended output has the same purpose and is of the same nature as its copyrighted inputs. 1 Incoming Associate, Betts Patterson & Mines, 2024; J.D., University of Washington School of Law, 2024; Washington Journal of Law Technology & Art; B.A. The Evergreen State College, 2017. 2 J.D. Candidate, University of Washington School of Law, Class of 2024. B.S., University of Washington, 2021, Chief Articles Editor, Washington Journal of Law, Technology, and the Arts. This author would like to extend his thanks to Professors Ryan Cato and Zahr K. Said for their assistance in developing the thesis for this article and guiding our research and analysis throughout. 3 J.D. Candidate, University of Washington School of Law, 2024; Washington Law Review, Executive Articles Editor; B.S., Stanford University, 2011; M.S., Stanford University, 2012. We would like to thank Professors Ryan Cato and Zahr Said for their support and feedback throughout the drafting process. We're also grateful to the 2023 IP Works In Progress Conference attendees for their encouragement. Finally, thank you to Kiara Hildeman and the wonderful editors at WJLTA for helping us finalize the Article.

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