Racialized Surveillance and Predictive Policing

Sage Kim, University of Illinois at Chicago

Surveillance has always been a technique of social control, often results in highly racialized monitoring and containment of minorities. The emergence of big data and predictive analytics in recent years has transformed the ways in which people and places are classified and monitored. Big data predictive policing invokes the premise of risk management which aims to mitigate future harms. Although the utilization of big data is assumed to bring about more effective (data driven) prediction (thus allowing prevention), predictive policing has shown to be not very effective in predicting future crime. But it is proven to be highly effective in quickly sorting people based on their risk, which then can be used to justify reasons for concentrated surveillance in certain areas and groups of people. We show how the Chicago Police Department (CPD)’s predictive policing since the 2000s has built a data system that captures an extremely large number of black and Hispanic Chicago residents and also narrows to highly select/concentrated Chicago community areas. We discuss three key problems with this risk management perspective in predictive policing are: 1) by definition, risk is always about events that have not happened yet; 2) due to big data technology, an unprecedentedly large number of people (data) can be processed, classified, and surveilled; and 3) assemblage of multiple data sources allows targeted surveillance of “at-risk” populations. Consequently, big data predictive policing is one of the most recent devices for social control, which justifies racialized policing.

No extended abstract or paper available

 Presented in Session 155. Policing in Chicago: Big Data and Racialized Surveillance