The Relative Virtues of Bottom-Up and TopDown Theories of Fair Use
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Some conceptions of the fair use limitation of US copyright law have their groundings in the case law out of which the doctrine emerged. (I call these the “bottom-up” approaches.) Other theories of fair use have sprung from the very bright minds of copyright scholars whose collective goal has generally been to bring some needed coherence to the common law of fair use. (I call these “top-down” approaches.) The Dual-Grant Theory of Fair Use is the latest of the top-down theories to have appeared in the literature.
Critics of generative AI often describe it as a “plagiarism machine.” They may be right, though not in the sense they mean. With rare exceptions, generative AI doesn’t just copy someone else’s creative expression, producing outputs that infringe copyright. But it does get its ideas from somewhere. And it’s quite bad at identifying the source of those ideas. That means that students (and professors, and lawyers, and journalists) who use AI to produce their work generally aren’t engaged in copyright infringement. But they are often passing someone else’s work off as their own, whether or not they know it. While plagiarism is a problem in academic work generally, AI makes it much worse because authors who use AI may be unknowingly taking the ideas and words of someone else.
Disclosing that the authors used AI isn’t a sufficient solution to the problem because the people whose ideas are being used don’t get credit for those ideas. Whether or not a declaration that “AI came up with my ideas” is plagiarism, failing to make a good-faith effort to find the underlying sources is a bad academic practice.
We argue that AI plagiarism isn’t—and shouldn’t be—illegal. But it is still a problem in many contexts, particularly academic work, where proper credit is an essential part of the ecosystem. We suggest best practices to align academic and other writing with good scholarly norms in the AI environment.
Critics of generative AI often describe it as a “plagiarism machine.” They may be right, though not in the sense they mean. With rare exceptions, generative AI doesn’t just copy someone else’s creative expression, producing outputs that infringe copyright. But it does get its ideas from somewhere. And it’s quite bad at identifying the source of those ideas. That means that students (and professors, and lawyers, and journalists) who use AI to produce their work generally aren’t engaged in copyright infringement. But they are often passing someone else’s work off as their own, whether or not they know it. While plagiarism is a problem in academic work generally, AI makes it much worse because authors who use AI may be unknowingly taking the ideas and words of someone else.
Disclosing that the authors used AI isn’t a sufficient solution to the problem because the people whose ideas are being used don’t get credit for those ideas. Whether or not a declaration that “AI came up with my ideas” is plagiarism, failing to make a good-faith effort to find the underlying sources is a bad academic practice.
We argue that AI plagiarism isn’t—and shouldn’t be—illegal. But it is still a problem in many contexts, particularly academic work, where proper credit is an essential part of the ecosystem. We suggest best practices to align academic and other writing with good scholarly norms in the AI environment.
I would like to thank Professor Jonathan Masur and the editors and staff of The University of Chicago Law Review, including Andy Wang, Zoë Ewing, Jonah Klausner, Karan Lala, Eric Haupt, Eugene DeCosse, and Helen Chamberlin, for their thoughtful advice and insights.
Holders of patents covering technology standards, known as standard-essential patents (SEP), control the rights to an invention with no commercially-viable alternative or that cannot be designed around while still complying with a standard. This gives SEP holders significant leverage in licensing negotiations. Standards development organizations (SDOs) play an important role in curbing opportunistic behavior by patent holders. SDOs require SEP holders to license their patents on fair, reasonable, and non-discriminatory (FRAND) terms. However, courts have mischaracterized FRAND commitments, concluding that these disputes carry a Seventh Amendment guarantee to a jury trial. This mischaracterization undermines the fair resolution of FRAND disputes, and a different approach is necessary. In this Comment, Marta Krason proposes an alternative analytical framework that more accurately characterizes FRAND disputes by drawing on principles from contract and property law, concluding that the constitutionally proper adjudicator is a judge, not a jury.