Algorithmic prediction in policing: assumptions, evaluation, and accountability

The goal of predictive policing is to forecast where and when crimes will take place in the future. The idea has captured the imagination of law enforcement agencies around the world. Many agencies are purchasing software tools with the goal of reducing crime by mapping the likely locations of future crime to guide the deployment of police resources. Yet the claims and promises of predictive policing have not been subject to critical examination. This paper provides a review of the theories, techniques, and assumptions embedded in various predictive tools and highlights three key issues about the use of algorithmic prediction. Assumptions: The algorithms used to gain predictive insights build on assumptions about accuracy, continuity, the irrelevance of omitted variables, and the primary importance of particular information (such as location) over others. In making decisions based on these algorithms, police are also directed towards particular kinds of decisions and responses to the exclusion of others. Evaluation: Media coverage of these technologies implies that they are successful in reducing crime. However, these claims are not necessarily based on independent, peer reviewed evaluations. While some evaluations have been conducted, additional rigorous and independent evaluations are needed to understand more fully the effect of predictive policing programmes. Accountability: The use of predictive software can undermine the ability for individual officers or law enforcement agencies to give an account of their decisions in important ways. The paper explores how this accountability gap might be reduced.