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.
he efforts come at a volatile time in Chicago and around the country. The Chicago Police Department is under a federal probe after last year’s release of video showing an officer fatally shooting Laquan McDonald 16 times in October 2014. The release of another video earlier this month, from the scene of a July stolen car crash in which police fatally shot 18-year-old Paul O’Neal in the back, further roiled relations between the community and its police force.
Those incidents were followed by weekend rioting in Milwaukee after a police officer shot and killed a man who reportedly refused to drop his gun during a foot chase.
While the police misconduct application is one of the more controversial elements of this version of big-data processing, the researchers say their goal is broader.
“The thing we’re finding is that using it (big data) to predict officer adverse incidents is just one use,” said Rayid Ghani, director of the Center for Data Science & Public Policy and previously chief data scientist for President Barack Obama’s 2012 campaign. “Inside police departments, they are doing a lot of other things — performance management, other safety things, training. This is easily extensible to all those things.”