Societal Consequences of Biased Data in Predictive Policing
We demonstrate this effect by applying a recent predictive policing model to data on drug crimes in the City of Oakland, CA*. We find that the occurrence of drug related arrests are highly concentrated in areas with higher proportions of low income and non-white residents. When the predictive policing model is applied to this data, additional targeted policing is directed primarily to the the non-white and low-income neighborhoods, despite the fact that public health surveys suggest that drug use in these areas are no more common than in more more affluent, white neighborhoods.
Our case study suggests that predictive policing models not only perpetuate existing racial and income discrimination in policing but create a feedback loop that amplifies the police targeting of low-income and minority residents.