A debiasing technique for place-based algorithmic patrol management
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866914638966292480 |
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| author | Einarsson, Alexander Oestmo, Simen Wollman, Lester Purves, Duncan Jenkins, Ryan |
| author_facet | Einarsson, Alexander Oestmo, Simen Wollman, Lester Purves, Duncan Jenkins, Ryan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06162 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A debiasing technique for place-based algorithmic patrol management Einarsson, Alexander Oestmo, Simen Wollman, Lester Purves, Duncan Jenkins, Ryan Computers and Society Artificial Intelligence 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. |
| title | A debiasing technique for place-based algorithmic patrol management |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2401.06162 |