Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It

Sara B. Heller, Benjamin Jakubowski, Zubin Jelveh and Max Kapustin

No 30170, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, we train a machine learning model to predict the risk of being shot in the next 18 months. Out-of-sample accuracy is strikingly high: of the 500 people with the highest predicted risk, almost 17 percent are shot within 18 months, a rate 106 times higher than the average Chicagoan. A central concern with using police data is that predictions will “bake in” bias, overestimating risk for groups likelier to interact with police conditional on behavior. We show that Black male victims more often have enough police contact to generate predictions. But those predictions are not, on average, inflated; the demographic composition of predicted and actual shooting victims is almost identical. There are legal, ethical, and practical barriers to using these predictions to target law enforcement. But using them to target social services could increase the potential for prevention programs to reduce shootings: predictive accuracy among the top 500 people justifies spending up to $190,900 per person for an intervention that could cut the probability of being shot by half.

JEL-codes: C53 H75 I14 K42 (search for similar items in EconPapers)
Date: 2022-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-ure
Note: EH LE PE
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