Precise Event-level Prediction of Urban Crime Reveals Signature of Enforcement Bias Precise Event-level Prediction of Urban Crime Reveals Signature of Enforcement Bias

11 Policing efforts to thwart urban crime often rely on detailed reports of criminal infractions. However, 12 crime rates do not document the distribution of crime in isolation, but rather its complex relationship 13 with policing and society. Several results attempting to predict future crime now exist, with varying 14 degrees of predictive efficacy. However, the very idea of predictive policing has stirred controversy, 15 with the algorithms being largely black boxes producing little to no insight into the social system 16 of crime, and its rules of organization. The issue of how enforcement interacts with, modulates, and 17 reinforces crime has been rarely addressed in the context of precise event predictions. In this study, 18 we demonstrate that while predictive tools have often been designed to enhance state power through 19 surveillance, they also enable the tracing of systemic biases in urban enforcement-surveillance of 20 the state. We introduce a novel stochastic inference algorithm as a new forecasting approach that 21 learns spatio-temporal dependencies from individual event reports with demonstrated performance far 22 surpassing past results (e.g., average AUC of % WH7 in the City of Chicago for property and violent 23 crimes predicted a week in advance within spatial tiles % IHHH ft across). These precise predictions enable 24 equally precise evaluation of inequities in law enforcement, discovering that response to increased crime 25 rates is biased by the socioeconomic status of neighborhoods, draining policy resources to wealthy 26 areas with disproportionately negative impacts for the inner city, as demonstrated in Chicago and six 27 other major U.S. metropolitan areas. While the emergence of powerful predictive tools raise concerns 28 regarding the unprecedented power they place in the hands of over-zealous states in the name of civilian 29 protection, our approach demonstrates how sophisticated algorithms enable us to audit enforcement 30 biases, and hold states accountable in ways previously inconceivable. 31