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.

打击犯罪的警务工作通常依赖于犯罪行为报告，这隐含地表明了犯罪、警务和社会之间的复杂关系。因此，犯罪预测和预测性警务引发了争议，最新的基于人工智能的算法对犯罪社会系统的洞察力有限。在这里，我们表明，虽然预测模型可以通过刑事监视来增强国家权力，但它们也可以通过追踪犯罪执法中的系统性偏见来实现对国家的监视。我们引入了一种随机推理算法，该算法通过从事件报告中学习时空相关性来预测犯罪，对于每周预测在 ~1,000 英尺内的犯罪，芝加哥的接收器操作特征曲线下的平均面积约为 90%。