Richard Berk, Ph.D., is a Professor of Criminology in the School of Arts & Sciences and a Professor of Statistics in Wharton. Professor Berk has conducted research on policy related to domestic violence for over 30 years. He co-authored the article about the Minneapolis Experiment, which led police departments across the country to change their policies to arrest batterers, rather than try to "cool down" and then let them go. His most recent policy-related work on domestic violence involves the use of machine learning to help inform judges' decisions regarding pre-trial release and police officer decisions regarding how to handle cases of domestic violence.
Click here to watch his 2013 Chicago Ideas Week talk on forecasting criminal behavior and crime victimization.
RECENT SELECT PUBLICATIONS
An impact assessment of machine learning risk forecasts on parole board decisions and recidivism. Journal of Experimental Criminology, 2017; 13: 193-216.
Forecasting domestic violence: A machine learning approach to help inform arraignment decisions, (with Susan B. Sorenson and Geoffrey Barnes), Journal of Empirical Legal Studies, 2016; 13: 94-115.
Machine learning forecasts of risk to inform sentencing decisions, (with Jordan Hyatt), The Federal Sentencing Reporter, forthcoming, 2015.
Covariance adjustments for the analysis of randomized field experiments, (with Emil Pitkin, Lawrence Brown, Andreas Buja, Edward George, and Linda Zhao), Evaluation Review, 2014; 37: 170-196.
Misspecified mean function regression: Making good use of regression models that are wrong, (with Lawrence Brown, Andreas Buja, Edward George, Emil Pitkin, Kai Zhang, and Linda Zhao), Sociological Methods and Research, 2014; 43: 422-451.
Click here for Prof. Berk's website.