Diag Davenport is a behavioral economist studying technological and social problems that drive inequality. He is currently an Assistant Professor of Technology Policy, Governance, and Society at UC Berkeley with appointments in the Goldman School of Public Policy and the School of Information.
He conducts research in three areas: empowering good ideas, responsible AI, and cultural evolution. His work develops theory by blending natural, field, and lab experiments. He typically focuses on applications relevant to criminal justice reform, tech policy, and the future of work.
Diag is a co-recipient of the 2023 Hillel Einhorn New Investigator Award from the Society of Judgment and Decision Making. Diag’s work has been published in top journals such as Proceedings of the National Academy of Sciences and Nature Human Behavior. His work has also been featured in popular press venues—Bloomberg, Boston Globe, Forbes, and The Telegraph—as well as cited in reports by OfCom and The World Bank. He is co-organizing the NeurIPS Workshop on Behavioral Machine Learning.
Before joining UC Berkeley, Diag was a Presidential Postdoctoral Research Fellow at Princeton. Diag earned his PhD in Behavioral Science from the University of Chicago Booth School of Business. He also has a master’s in Mathematics and Statistics from Georgetown and bachelor’s degrees in Economics and Management from Penn State. Prior to his PhD studies, Diag advised insurance and manufacturing executives on future litigation risks as a consultant at Bates White and built a variety of data products as a data scientist for the DC Superintendent of Education, the Federal Reserve Board, and a Covid-thwarted travel startup. During his PhD studies, he also operated an AI consulting firm that advised several startups on data strategy. Those clients have gone on to collectively raise over $20 million.