Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial
Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial
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DOI：
10.1080/2330443x.2018.1438940
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发表时间：
2018-02-08
影响因子：
1.6
通讯作者：
Mohler, George O.
中科院分区：
文献类型：
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作者：
Brantingham, P. Jeffrey;Valasik, Matthew;Mohler, George O.
预测警务算法中的种族偏见一直是最近一些新闻文章的焦点，一些国家组织（例如，美国公民自由联盟和全国有色人种协进会），以及基于模拟的研究。人们有理由担心，预测算法会鼓励警察直接巡逻，以少数群体为目标，对少数群体个人造成歧视性后果。然而，到目前为止，还没有实证研究预测算法用于警察巡逻的偏见。在这里，我们使用来自洛杉矶预测警务实验的逮捕数据来测试这种偏见。我们发现，控制和治疗条件之间的种族-民族组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，师级逮捕的总人数下降或保持不变。在算法预测的地点，逮捕人数在数字上更高。然而，当根据算法预测地点的整体犯罪率较高进行调整时，逮捕率较低或保持不变。
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.