Understanding law enforcement and common peoples' perspectives on designing explainable crime mapping algorithms

Abstract

In recent years, with growing concerns of making predictive policing less-biased and less-risky, the HCI and CSCW research communities have focused on designing more explainable and accountable algorithms in the criminal justice system. In this extended abstract, we present a preliminary, qualitative analysis of the perceptions of people with different backgrounds (n=60) from Milwaukee, USA on algorithmic crime mapping. Our initial results suggest the need for algorithmic interaction and the database transparency of the system. Taken these suggestions together will inspire to design an explainable crime mapping algorithms that pay attention to the values and needs of law enforcement and common peoples.