Scientists develop machine learning tool to better predict crime

A group of social and data scientists developed a machine learning tool it hoped would better predict crime. For once, algorithms that predict crime might be used to uncover bias in policing, instead of reinforcing it. The scientists say they succeeded, but their work also revealed inferior police protection in poorer neighborhoods in eight major U.S. cities, including Los Angeles. Chattopadhyay said previous efforts at crime prediction didn’t always account for systemic biases in law enforcement and were often based on flawed assumptions about crime and its causes. “Rather than simply increasing the power of states by predicting the when and where of anticipated crime, our tools allow us to audit them for enforcement biases, and garner deep insight into the nature of the (intertwined) processes through which policing and crime co-evolve in urban spaces,” their report said. The tool, developed by a team led by University of Chicago professor Ishanu Chattopadhyay, forecasts crime by spotting patterns amid vast amounts of public data on property crimes and crimes of violence, learning from the data as it goes. Crime in poor neighborhoods didn’t always lead to more arrests — suggesting “biases in enforcement,” the researchers concluded. They focused on specific “hot spots,” while failing to take into account the complex social systems of cities or the effects of police enforcement on crime, he said. (Los Angeles Times). Continue reading.



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