Data science confronts law enforcement.

• Position: Policing

Amidst increasing calls for changes in policing, departments across the country are searching for new ways to build trust and protect citizens and officers alike. Police in Charlotte, N.C., and Nashville, Tenn., have turned to the White House-backed Police Data Initiative at the University of Chicago (Ill.) that uses advanced data analytics to predict and prevent adverse incidents, ranging from excessive force to officer injury.

Led by the Center for Data Science and Public Policy at the Computation Institute and School of Public Policy, the initiative applies machine-learning methods to police department data to identify officers and police calls at a higher risk of producing adverse events, such as the use of excessive force or a sustained citizen complaint. The predictive models can be used to guide personalized interventions for at-risk officers or adjust dispatch procedures to reduce high-stress situations.

"The goal is to take historical data about these police officers--their behaviors, citations, arrests, dispatches--and use that data to assign each officer a risk score," explains Rayid Ghani, the center's director. "That risk score is then used to...