The practice of predicting crime using AI has a long history of skewing police toward communities of color. But that hasn’t stopped researchers from building crime-predicting tools.
The algorithm identifies locations in major cities that it calculates have a high likelihood of crimes, like homicides and burglaries, occurring in the next week. The software can also evaluate how policing varies across neighborhoods in eight major cities in the United States, including Chicago, Los Angeles and Philadelphia.
But algorithms are only as good as the data they are fed, which is a problem particularly for people in the United States, said Vincent Southerland, the co-faculty director of New York University’s Center on Race, Inequality and the Law.Historically, police data in the United States is biased, according to Southerland.
its crime prediction program, LASER, which used historical crime data to predict crime hotspots and Palantir software to assign people criminal risk scores, after an internal audit showed it led to police to unfairly subject Black and Latino people to more surveillance. In making the algorithm, Chattopadhyay’s team segmented major cities into 1,000 square foot city blocks and used city crime data from the last three to five years to train it. The algorithm spits out whether there is a high or low risk of crime happening in a segment at a certain time, up to one week into the future.
Arvind Narayanan, a computer science professor at Princeton University, said the study’s news release and news articles about it did not focus enough on the study’s attempt to investigate biases in police crime enforcement and overemphasized the algorithms’ accuracy claims.
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