A debiasing technique for place-based algorithmic patrol management
This addresses fairness issues in algorithmic policing for law enforcement and affected communities, but it appears incremental as an exploratory work with future research directions.
The paper tackled bias in data-driven policing by introducing a debiasing technique for place-based algorithmic patrol management, showing it efficiently eliminates racially biased features while retaining high model accuracy.
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.