Diag Davenport is an Assistant Professor of Technology Policy, Governance, and Society at UC Berkeley. His work examines how institutions make decisions about people, and how people make decisions within institutions. Drawing on economics, psychology, machine learning, law, and public-interest data practice, his research studies the rules, technologies, incentives, and representations that shape judgment, agency, and responsibility in public life.
A recurring theme in his work is that institutions govern through representations of reality: scores, forms, rules, thresholds, queues, prices, legal texts, interfaces, and roles. These representations make coordination possible, but they can also hide discretion, narrow agency, and route public life toward compliance, confusion, or conflict. Diag studies how these systems are designed, how people navigate them, and how they can be made more legible, contestable, and accountable.
He leads the Local Laws Observatory, a project making local law systematically observable across the United States, and the Public Interest Technology Clinic, which trains students to build useful technical systems inside real public institutions while critically examining what those systems see, miss, and change.
Diag is the co-recipient of the 2023 Hillel Einhorn New Investigator Award from the Society for Judgment and Decision Making. His research has appeared in outlets including Nature Human Behaviour, PNAS, and FAccT, and has been featured in major media and policy reports. Before joining Berkeley, he was a Presidential Postdoctoral Research Fellow at Princeton University. He received his PhD in Behavioral Science from the University of Chicago Booth School of Business, an MS in Mathematics and Statistics from Georgetown University, and bachelor’s degrees in Economics and Management from Penn State.