How Stanford Researchers are Designing Fair and Trustworthy AI Systems
(Originally published by Stanford Report on July 29, 2025.)
AI and Law
“When we purchased our home, I had to sign a document that said that the ‘property shall not be used or occupied by any person of African, Japanese or Chinese or any Mongolian descent,’” said Daniel E. Ho, the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School.
The experience underscored for him how deeply racism is embedded in legal infrastructure – something he’s now working to help dismantle through technology. Ho directs Stanford’s RegLab, which partners with government agencies to explore how AI can improve policy, services, and legal processes.
Recently, RegLab worked with Santa Clara County, which was implementing a state law that mandated all counties identify and redact racist property deeds.
“For Santa Clara County, this meant revising about 84 million pages of records dating back to the 1800s,” Ho said. “We developed an AI system to enable the county to spot, map, and redact deed records, saving some 86,500 person hours.”
Procedural bloat – an issue highlighted by both Democrats and Republicans – is another problem RegLab addressed with AI.
“One of the odd areas of consensus right now is that government processes aren’t working terribly well,” said Ho. “We developed a Statutory Research Assistant (STARA) AI system that can identify obsolete requirements, such as reports that can needlessly consume tons of staff time.”
San Francisco City Attorney David Chiu introduced a resolution to cut over a third of these requirements based on the RegLab collaboration.
“Implemented responsibly, I think AI has tremendous potential to improve government programs and access to justice,” said Ho.