Policing efforts to thwart urban crime often rely on detailed reports of criminal infractions. However, crime rates do not document the distribution of crime in isolation, but rather its complex relationship with policing and society. Several results attempting to predict future crime now exist, with varying degrees of predictive efficacy. However, the very idea of predictive policing has stirred controversy, with the algorithms being largely black boxes producing little to no insight into the social system of crime, and its rules of organization. The issue of how enforcement interacts with, modulates, and reinforces crime has been rarely addressed in the context of precise event predictions. In this study, we demonstrate that while predictive tools have often been designed to enhance state power through surveillance, they also enable the tracing of systemic biases in urban enforcement-surveillance of the state. We introduce a novel stochastic inference algorithm as a new forecasting approach that learns spatio-temporal dependencies from individual event reports with demonstrated performance far surpassing past results (e.g., average AUC of ✙ ✾✵✪ in the City of Chicago for property and violent crimes predicted a week in advance within spatial tiles ✙ ✶✵✵✵ ft across). These precise predictions enable equally precise evaluation of inequities in law enforcement, discovering that response to increased crime rates is biased by the socio-economic status of neighborhoods, draining policy resources to wealthy areas with disproportionately negative impacts for the inner city, as demonstrated in Chicago and six other major U.S. metropolitan areas. While the emergence of powerful predictive tools raise concerns regarding the unprecedented power they place in the hands of over-zealous states in the name of civilian protection, our approach demonstrates how sophisticated algorithms enable us to audit enforcement biases, and hold states accountable in ways previously inconceivable.T HE emergence of large-scale data and ubiquitous data-driven modeling has sparked widespread government interest in the possibility of predictive policing [1][2][3][4][5] : predicting crime before it happens to enable anticipatory enforcement. Such efforts, however, do not document the distribution of crime in isolation, but rather its complex relationship with policing and society. In this study, we reconceptualize the process of crime prediction, build novel methods to improve it, and use it to diagnose both the distribution of reported crime and biases in its enforcement. The history of statistics has co-evolved with the history of criminal prediction, but also with the history of enforcement critique. Sim éon Poisson published the Poisson distribution and his theory of probability in an analysis of the number of wrongful convictions in a given country 6 . Andrey Markov introduced Markov processes to show that dependencies between outcomes could still obey the central limit theorem to counter Pavel Nekrasov's argument that because Russian crime r...
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