犯罪学
弱势群体
执法
预测能力
推论
追踪
预防犯罪
邻里(数学)
执行
计算机安全
计量经济学
计算机科学
政治学
人工智能
心理学
经济
法学
数学
哲学
数学分析
操作系统
认识论
作者
Victor Rotaru,Yi Huang,Timmy Li,James A. Evans,Ishanu Chattopadhyay
标识
DOI:10.1038/s41562-022-01372-0
摘要
Policing efforts to thwart crime typically rely on criminal infraction reports, which implicitly manifest a complex relationship between crime, policing and society. As a result, crime prediction and predictive policing have stirred controversy, with the latest artificial intelligence-based algorithms producing limited insight into the social system of crime. Here we show that, while predictive models may enhance state power through criminal surveillance, they also enable surveillance of the state by tracing systemic biases in crime enforcement. We introduce a stochastic inference algorithm that forecasts crime by learning spatio-temporal dependencies from event reports, with a mean area under the receiver operating characteristic curve of ~90% in Chicago for crimes predicted per week within ~1,000 ft. Such predictions enable us to study perturbations of crime patterns that suggest that the response to increased crime is biased by neighbourhood socio-economic status, draining policy resources from socio-economically disadvantaged areas, as demonstrated in eight major US cities.
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