# A debiasing technique for place-based algorithmic patrol management

arXiv: https://arxiv.org/abs/2401.06162 · HTML: https://mlsanity.com/paper/2401.06162/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)

## Built on by (most cited first, 1 of 1)

- [Community-Informed AI Models for Police Accountability](https://mlsanity.com/paper/2402.01703/) (2024)

## Abstract (arXiv)

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.