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110 Cornell L. Rev. 1 (2025)

handle is hein.journals/clqv110 and id is 1 raw text is: SYNTHETIC DATA AND THE FUTURE OF Al Peter Leet Thefuture of artificial intelligence (Al) is synthetic. Several of the most prominent technical and legal challenges of AI derivefrom the need to amass huge amounts of real-world data to train machine learning (ML) models. Collecting such real- world data can be highly difficult and can threaten privacy, introduce bias in automated decision making, and infringe copyrights on a massive scale. This Article explores the emer- gence of a seemingly paradoxical technical creation that can mitigate-though not completely eliminate-these concerns: synthetic data. Increasingly, data scientists are using simu- lated driving environments, fabricated medical records, fake images, and other forms of synthetic data to train ML models. Artificial data, in other words, is training artificial intelligence. Synthetic data offers a host of technical and legal benefits; it promises to radically decrease the cost of obtaining data, side- step privacy issues, reduce automated discrimination, and avoid copyright infringement. Alongside such promises, how- ever, synthetic data offers perils as well. Deficiencies in the development and deployment of synthetic data can exacerbate the dangers of AI and cause significant social harm. In light of the enormous value and importance of syn- thetic data, this Article sketches the contours of an innovation ecosystem to promote its robust and responsible develop- ment. It identifies three objectives that should guide legal and policy measures shaping the creation of synthetic data: provisioning, disclosure, and democratization. Ideally, such an ecosystem should incentivize the generation of high-quality t Martin Luther King Jr. Professor of Law and Director, Center for Innova- tion, Law, and Society, UC Davis School of Law. I would like to thank Elizabeth Joh, Mark Lemley, Sarah Polcz, and workshop participants at the Lewis & Clark Fall Forum, the UC Davis-Jindal Global Law School symposium, the Transat- lantic Tech Exchange Roundtable hosted by the German Marshall Fund, the UC Davis School of Law Schmooze, the Intellectual Property Scholars Conference at UC Berkeley School of Law, and the University of Texas School of Law for very helpful comments. I would also like to thank Dean Kevin Johnson and Senior Associate Dean Afra Afsharipour for providing generous institutional support for this project. This research was supported by a grant from the UC Davis Academic Senate Committee on Research. My thanks as well to McKenzie Deutsch and the UC Davis School of Law Library staff for excellent research assistance. I would also like to thank the outstanding editors of the Cornell Law Review. 1

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