ABSTRACT Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulation-based research. There is reasonable concern that predictive algorithms encourage directed police patrols to target minority communities with discriminatory consequences for minority individuals. However, to date there have been no empirical studies on the bias of predictive algorithms used for police patrol. Here, we test for such biases using arrest data from the Los Angeles predictive policing experiments. We find that there were no significant differences in the proportion of arrests by racial-ethnic group between control and treatment conditions. We find that the total numbers of arrests at the division level declined or remained unchanged during predictive policing deployments. Arrests were numerically higher at the algorithmically predicted locations. When adjusted for the higher overall crime rate at algorithmically predicted locations, however, arrests were lower or unchanged.

中文翻译：

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预测性警务会导致有偏见的逮捕吗？随机对照试验的结果

摘要预测性警务算法中的种族偏见已成为许多近期新闻，一些国家组织（例如ACLU和NAACP）关注的声明以及基于仿真的研究的焦点。合理的关注是，预测算法会鼓励定向警察巡逻以少数民族社区为目标，并对少数民族造成歧视性后果。然而，迄今为止，还没有关于用于警察巡逻的预测算法的偏差的经验研究。在这里，我们使用来自洛杉矶预测性警务实验的逮捕数据来测试这种偏见。我们发现，在控制和治疗条件之间，不同种族种族的逮捕比例没有显着差异。我们发现，在预测性警务部署期间，部门级别的逮捕总数有所下降或保持不变。在算法上预测的位置，逮捕的人数更高。但是，如果根据算法预测的地点的较高总体犯罪率进行调整，则逮捕人数会减少或保持不变。