Increasing calls for changes in policing have departments across the country searching for new ways to build trust and protect citizens and officers alike. Led by the Center for Data Science and Public Policy at the Computation Institute and Harris 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," said Rayid Ghani, the center's director. "That risk score is then used to predict which officers are at risk of one of these adverse incidents, and provide the police departments with this information so they can then work on interventions to prevent these incidents."