A debiasing technique for place-based algorithmic patrol management
Computers and Society
2024-01-15 v1 Artificial Intelligence
Abstract
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.
Cite
@article{arxiv.2401.06162,
title = {A debiasing technique for place-based algorithmic patrol management},
author = {Alexander Einarsson and Simen Oestmo and Lester Wollman and Duncan Purves and Ryan Jenkins},
journal= {arXiv preprint arXiv:2401.06162},
year = {2024}
}
Comments
20 pages (91 Appendix pages), 6 figures (20 supplementary figures), 14 supplementary tables