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U. of C. Researchers Use Data to Predict Police Misconduct

By Rob Mitchum // August 18, 2016

For the last two summers, fellows at Data Science for Social Good and researchers at the Center for Data Science and Public Policy (DSaPP) have worked with police departments around the country on developing a data-driven model for predicting police officer behavior. Today, the Chicago Tribune featured the project, speaking with DSaPP director Rayid Ghani, DSaPP project manager Lauren Haynes, and representatives from DSSG partners at the Charlotte-Mecklenburg and Metro Nashville police departments. Reporter Ted Gregory explains how these models can help departments identify officers in need of additional training, reducing adverse incidents with the public and improving trust between police and hte public.

Advocates of the data-driven approach agree that its success depends on reliable and extensive data. The quality of data is improving and the capacity for processing that legitimate data is rapidly becoming more sophisticated, supporters say.[In oversight of Chicago police, IPRA gives victims false sense of justice

In addition, the U. of C.’s data science teams have visited Charlotte-Mecklenburg police several times, participating in ride-alongs and officer focus groups seeking their input on what factors may predict an officer having an adverse interaction. Blending that context with the higher quality data processing has made the newer system even more accurate, U. of C.’s analysts say.

“There’s a lot of human intuition in it,” said Lauren Haynes, senior project manager at the Center for Data Science & Public Policy. She added that Charlotte-Mecklenburg officers who once were suspicious of the data program have welcomed the chance to share their perspective.

Early tests from modeling have yielded encouraging results. Compared with the Charlotte Police Department’s existing threshold-based system, the Data Science & Public Policy system accurately flagged more officers who went on to have adverse interventions, Patterson said.

“That was an indication that we’re going in the right direction,” she said. She emphasized that the proposed system “is not punitive in any fashion. They’re early warnings that alert us.”

To read the rest of the article, visit the Chicago Tribune. For more on the police project, check out Data Science for Social Good.

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