  In recent years, there has been a revolution in data-driven policing. With
that has come scrutiny on how bias in historical data affects algorithmic
decision making. In this exploratory work, we introduce a debiasing technique
for place-based algorithmic patrol management systems. We show that the
technique efficiently eliminates racially biased features while retaining high
accuracy in the models. Finally, we provide a lengthy list of potential future
research in the realm of fairness and data-driven policing which this work
uncovered.
