Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It / Sara B. Heller, Benjamin Jakubowski, Zubin Jelveh, Max Kapustin.

Format:

Book

Author/Creator:

Heller, Sara B.

Contributor:

National Bureau of Economic Research.

Jakubowski, Benjamin.

Jelveh, Zubin.

Kapustin, Max.

Series:

Working Paper Series (National Bureau of Economic Research) no. w30170.

NBER working paper series no. w30170

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2022.

Summary:

This paper shows that shootings are predictable enough to be preventable. 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 13 percent are shot within 18 months, a rate 128 times higher than the average Chicagoan. A central concern is that algorithms may "bake in" bias found in police data, overestimating risk for people likelier to interact with police conditional on their 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 have enormous preventive benefits: predictive accuracy among the top 500 people justifies spending up to $134,400 per person for an intervention that could cut the probability of being shot by half.

Notes:

Print version record

June 2022.