| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w30170 |
| 来源ID | Working Paper 30170 |
| Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It | |
| Sara B. Heller; Benjamin Jakubowski; Zubin Jelveh; Max Kapustin | |
| 发表日期 | 2022-06-20 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | 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. We address central concerns about police data and algorithmic bias by predicting shooting victimization rather than arrest, which we show accurately captures risk differences across demographic groups despite bias in the predictors. Out-of-sample accuracy is strikingly high: of the 500 people with the highest predicted risk, 13 percent are shot within 18 months, a rate 130 times higher than the average Chicagoan. Although Black male victims more often have enough police contact to generate predictions, 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 $123,500 per person for an intervention that could cut their risk of being shot in half. |
| 主题 | Econometrics ; Estimation Methods ; Subnational Fiscal Issues ; Health, Education, and Welfare ; Health ; Other ; Law and Economics |
| URL | https://www.nber.org/papers/w30170 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587844 |
| 推荐引用方式 GB/T 7714 | Sara B. Heller,Benjamin Jakubowski,Zubin Jelveh,et al. Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It. 2022. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w30170.pdf（3295KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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