by Ted Gregory, Chicago Tribune
In two Loop office buildings about eight blocks apart, a pair of University of Chicago research teams are analyzing big data to answer a thorny question that has become especially charged in recent months: Will a police officer have an adverse interaction with a citizen?
The team from the university’s Crime Lab is in the first stages of working with the Chicago Police Department to build a predictive data program to improve the department’s Early Intervention System, which is designed to determine if an officer is likely to engage in aggressive, improper conduct with a civilian.
The other team, part of U. of C.’s Center for Data Science & Public Policy, is expected to launch a data-driven pilot of an Early Intervention System with the Charlotte-Mecklenburg Police Department in North Carolina by the end of the summer. The center is working on similar efforts with the Los Angeles County sheriff’s office and the Nashville and Knoxville police departments in Tennessee.
Data crunching has been used in policing since the late 1970s. But applying this level of big-data processing — similar to techniques that help determine email spam, a person’s movie preferences or advertisements on a social media page — to predict police misconduct is new, experts say. In this foray, data scientists are encountering deep suspicion from officers concerned about the system’s fairness and effectiveness. The new approach also raises the complex issue of what to do once the system predicts an officer is likely to misbehave.
© 2016 Chicago Tribune