Machine learning is proving to be a powerful tool in the fight against gun violence. A recent study from the National Bureau of Economic Research (NBER) demonstrates how predictive algorithms can identify individuals at high risk of being shot, enabling targeted interventions that could prevent victimization. By analyzing arrest and victimization records for nearly 644,000 people in Chicago, researchers trained a machine learning model to forecast the likelihood of shooting victimization over an 18-month period. The results are striking—of the 500 individuals identified as having the highest predicted risk, nearly 17% were shot within that timeframe, a rate 106 times higher than the average Chicago resident.
Read the full study here: https://www.nber.org/system/files/working_papers/w30170/w30170.pdf
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