Stone Inequality & Social Policy Seminar: Daniel Ho

AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County

Daniel E. Ho, Stanford University

Abstract: Legal reform can be challenging in light of the volume, complexity, and interdependence of laws, codes, and records. One salient example of this challenge is the effort to restrict and remove racially restrictive covenants, clauses in property deeds that historically barred individuals of specific races from purchasing homes. Despite the Supreme Court holding such racial covenants unenforceable in 1948, they persist in property records across the United States. Many jurisdictions have moved to identify and strike these provisions, including California, which mandated in 2021 that all counties implement such a process. Yet the scale can be overwhelming, with Santa Clara County (SCC) alone having over 24 million property deed documents, making purely manual review infeasible. We present a novel approach to addressing this pressing issue, developed through a partnership with the SCC Clerk-Recorder’s Office. First, we leverage an open large language model, fine-tuned to detect racial covenants with high precision and recall. We estimate that this system reduces manual efforts by 86,500 person hours and costs less than 2% of the cost for a comparable off-the-shelf closed model. Second, we illustrate the County’s integration of this model into responsible operational practice, including legal review and the creation of a historical registry, and release our model to assist the hundreds of jurisdictions engaged in similar efforts. Finally, our results reveal distinct periods of utilization of racial covenants, sharp geographic clustering, and the disproportionate role of a small number of developers in maintaining housing discrimination. We estimate that by 1950, one in four properties across the County were subject to racial covenants. 

Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law, Professor of Political Science, Professor of Computer Science (by courtesy), Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), Senior Fellow at the Stanford Institute for Economic Policy Research, and Director of the Regulation, Evaluation, and Governance Lab (RegLab) at Stanford University.

Ho has served on the National Artificial Intelligence Advisory Committee (NAIAC), advising the White House on artificial intelligence, as Senior Advisor on Responsible AI at the U.S. Department of Labor, on the Committee on National Statistics (CNSTAT) of the National Academies of Science, Engineering, and Medicine, and as a Public Member of the Administrative Conference of the United States (ACUS), and as Special Advisor to the ABA Task Force on Law and Artificial Intelligence. He is an elected member of the American Academy of Arts and Sciences. 

His scholarship focuses on administrative law, regulatory policy, and antidiscrimination law. With the RegLab, his work has developed high-impact demonstration projects of data science and machine learning in public policy, through partnerships with a range of government agencies, including the Internal Revenue Service, the Treasury Department, the Environmental Protection Agency, the Department of Labor, the San Francisco City Attorney’s Office, Santa Clara County, and Seattle and King County Public Health. 

He received his J.D. from Yale Law School and Ph.D. from Harvard University and clerked for Judge Stephen F. Williams on the U.S. Court of Appeals, District of Columbia Circuit. He is the recipient of numerous awards, including the John Bingham Hurlbut Award for Excellence in Teaching at Stanford Law School, the Carole Hafner Award for the best paper at the International Conference on Artificial Intelligence and Law (ICAIL), Best Paper awards at the annual meeting of the Association for Computational Linguistics (ACL), the ACM Conference on Fairness, Accountability, and Transparency (FAccT), and the ACM Conference Artificial Intelligence, Ethics, and Society (AIES), the SafeBench First Prize in AI Safety, the Best Empirical Paper Prize from the American Law and Economics Review, and the Warren Miller prize for the best paper published in Political Analysis.