Stanford’s Reg Lab is showing how AI can fix real-world city services without replacing human civil servants.
In this second episode of Working Theory, Jeff Keltner sits down with Derek Oyang, Research Director at Stanford’s Reg Lab (Regulation, Evaluation, and Governance Lab). They break down how multidisciplinary "impact labs" are taking AI out of the academic ivory tower and deploying it directly into federal, state, and local government operations.
Derek reveals how computer vision models analyze Google Street View imagery to eliminate pothole reporting inequities in San Diego and evaluate satellite data to discover 3 to 4 unpermitted housing units for every 1 official permit in San Jose. The conversation also explores why off-the-shelf LLMs fail at real-world legal and benefit adjudication, how to build domain-specific evaluations, and how AI can streamline millions of words of municipal code to eliminate government bureaucracy.
Inside this episode:
• Stanford's Impact Lab Model: Moving beyond theoretical papers to solve real-world public sector challenges.
• Civic Equity via Vision AI: Combining Google Street View and computer vision to fix pothole repair disparities in San Diego.
• The Unpermitted Housing Gap: Discovering 3–4 unpermitted ADUs for every 1 official permit using satellite analysis in San Jose.
• Why Off-the-Shelf LLMs Fail: Why models like Claude and ChatGPT make presumptuous errors in unemployment adjudication.
• Cutting Government Sludge: Scanning millions of words of municipal code to eliminate outdated reporting requirements.
• Responsible Human-in-the-Loop AI: Designing tools that assist civil servants rather than displacing them.