Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It
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.
Related
More from NBER
In addition to working papers, the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter, the NBER Digest, the Bulletin on Retirement and Disability, the Bulletin on Health, and the Bulletin on Entrepreneurship — as well as online conference reports, video lectures, and interviews.
