Data Science for Justice

Can data science improve the functioning of courts, and unlock the positive effects of institutions on development? In a nationwide experiment in Kenya, we use algorithms to identify the greatest sources of court delay for each court and recommend actions. We randomly assign courts to receive no information, information, or an information and accountability intervention. Information and accountability reduces case duration by 22%. Using continuous household surveys, we find that in regions with treated courts, workers were more likely to have formal contracts and higher wages, especially in contract-intensive industries. These results demonstrate a causal relationship between judicial institutions and economic development.

ABOUT THE SPEAKER

Daniel Li Chen is Director of Research at the CNRS and Professor at the Toulouse School of Economics. He is also a Senior Fellow at the IAST and the founder of oTree Open Source Research Foundation and Data Science Justice Collaboratory. Chen was previously Chair of Law and Economics and co-founder of Law and Economics Center at ETH; he was a tenure-track assistant professor in Law (primary), Economics, and Public Policy at Duke University.    

He received his BA (Summa Cum Laude, Phi Beta Kappa) and MS from Harvard University in Applied Mathematics and Economics; completed his Economics PhD from MIT; and obtained a JD from Harvard Law School.    

Chen uses his extensive empirical training to tackle longstanding legal questions previously difficult to empirically analyze. He has attained prominence through the development of open source tools to study human behavior and through large-scale empirical studies — data science, artificial intelligence, and machine learning — on the relationship between law, social norms, and the enforcement of legal norms, and on judicial systems.

Virtual to Public. Only those with an active Stanford ID with access to E008 in Encina Hall may attend in person.