Doubly Robust Internal Benchmarking and False Discovery Rates for Detecting Racial Bias in Police Stops

摘要

Allegations of racially biased policing are a contentious issue in many communities. Processes that flag potential problem officers have become a key component of risk management systems at major police departments. We present a statistical method to flag potential problem officers by blending three methodologies that are the focus of active research efforts: propensity score weighting, doubly robust estimation, and false discovery rates. Compared with other systems currently in use, the proposed method reduces the risk of flagging a substantial number of false positives by more rigorously adjusting for potential confounders and by using the false discovery rate as a measure to flag officers. We apply the methodology to data on 500,000 pedestrian stops in New York City in 2006. Of the nearly 3,000 New York City Police Department officers regularly involved in pedestrian stops, we flag 15 officers who stopped a substantially greater fraction of black and Hispanic suspects than our statistical benchmark predicts.