Jessie Allen (University of Pittsburgh) and Dasha Pruss (University of Illinois Chicago and Harvard University): Against AI Jurisprudence: Large Language Models and the False Promise of Empirical Judging

About the Speakers
Jessie Allen (she/her) is a Professor of Law at the University of Pittsburgh, where she teaches jurisprudence, legal ethics, election law, and property. Much of her scholarship considers how traditional legal authorities and practices can be reimagined to contribute to a socially progressive rule of law. Before coming to legal academia, Jessie was a voting rights attorney and before that a performance artist. As a senior attorney for Advancement Project, a racial justice organization, she litigated voting rights cases in swing states. At the Brennan Center for Justice she challenged state laws that prohibit people with criminal convictions from voting. Her essays have appeared in legal and interdisciplinary journals (e.g., Houston Law Review; Journal of Law and Courts; Emotions: History, Culture, Society; Tulane Law Review; Dissent). In the 1980s-90s, Jessie wrote and performed a series of solo and group pieces at downtown New York City venues, including Threadwaxing Space, Poetry Project at St. Marks, Downtown Art Co., and Franklin Furnace. She holds a doctorate in law (JSD) from Columbia University, a JD from Brooklyn Law School, and a BFA in theater from New York University.

Dasha Pruss (she/her) is an Assistant Professor of Philosophy at the University of Illinois Chicago and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. She received her PhD in History & Philosophy of Science from the University of Pittsburgh in 2023 and has a BS in computer science. Dasha’s research critically examines the societal implications of AI/ML systems, with a focus on algorithmic decision-making systems used in the US criminal legal system. She has written about recidivism risk assessment instruments, predictive policing tools, electronic monitoring, and other carceral technologies. In 2024, she organized Prediction and Punishment: Cross-Disciplinary Workshop on Carceral AI, which brought together scholars and activists from around the world to address technologies designed to police, incarcerate and surveil human beings.

About the Topic
As hype around the transformative effects of large language models (LLMs) has taken center stage in popular culture, some judges and legal scholars have suggested that LLMs have the potential to improve the objectivity of judicial decision-making. Proponents argue that using LLMs to find empirical ‘evidence’ of legal text’s meaning can reduce the role of judges’ subjective choices, ensuring that judicial rulings faithfully reflect the people’s understanding of legal rules, and grounding legal interpretation in a sophisticated empirical investigation of real language use in social context. To the contrary, we argue that LLM jurisprudence underscores the discretionary decisions required to infer ordinary meaning; highlights the inescapable reality that the meaning and application of legal terms is inherently normative; and demonstrates the lack of democratic legitimacy of crowdsourcing legal meaning. We argue that the feature of LLMs that makes them so seductive for legal interpretation—their potential ability to approximate ‘ordinary’ people’s understanding of legal text—reveals the political illegitimacy of empirical judging. We conclude with recommendations and warnings for practitioners in this space.

About the Speaker Series
The Speaker Series of the Minerva Fast Track Research Group “Artificial Justice” is organized by Katharina Isabel Schmidt. The Series invites guest speakers who work at the intersection between law, computer science, and the humanities. Neither technical nor juristic knowledge is a prerequisite for participation—the Series is aimed at anyone with an interest in critical and interdisciplinary perspectives on “Law and AI.” The event takes place on Zoom and is scheduled to last one hour.

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