Runaway Feedback Loops in Predictive Policing

TL;DR

This paper models and empirically demonstrates how predictive policing systems can create runaway feedback loops, and proposes interventions to prevent these loops, enabling more accurate crime rate learning and reducing disparities.

Contribution

The paper introduces a mathematical model explaining feedback loops in predictive policing and offers black-box interventions to mitigate these issues, improving system accuracy.

Findings

Runaway feedback loops are mathematically modeled and empirically validated.

Interventions can prevent feedback loops, allowing true crime rates to be learned.

Reported incidents alone cannot fully eliminate feedback effects.

Abstract

Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.