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Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases

An accurate crime prediction and risk estimation can help improve the efficiency and effectiveness of policing activities. However, reports have revealed that biases like racial prejudice could exist in policing enforcement, and trained predictors may inherit them. In this work, we study the possibl...

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Autores principales: Ren, Weijeiying, Liu, Kunpeng, Zhao , Tianxiang, Fu , Yanjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654242/
https://www.ncbi.nlm.nih.gov/pubmed/34901844
http://dx.doi.org/10.3389/fdata.2021.787459
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author Ren, Weijeiying
Liu, Kunpeng
Zhao , Tianxiang
Fu , Yanjie
author_facet Ren, Weijeiying
Liu, Kunpeng
Zhao , Tianxiang
Fu , Yanjie
author_sort Ren, Weijeiying
collection PubMed
description An accurate crime prediction and risk estimation can help improve the efficiency and effectiveness of policing activities. However, reports have revealed that biases like racial prejudice could exist in policing enforcement, and trained predictors may inherit them. In this work, we study the possible reasons and countermeasures to this problem, using records from the New York frisk and search program (NYCSF) as the dataset. Concretely, we provide analysis on the possible origin of this phenomenon from the perspective of risk discrepancy, and study it with the scope of selection bias. Motivated by theories in causal inference, we propose a re-weighting approach based on propensity score to balance the data distribution, with respect to the identified treatment: search action. Naively applying existing re-weighting approaches in causal inference is not suitable as the weight is passively estimated from observational data. Inspired by adversarial learning techniques, we formulate the predictor training and re-weighting as a min-max game, so that the re-weighting scale can be automatically learned. Specifically, the proposed approach aims to train a model that: 1) able to balance the data distribution in the searched and un-searched groups; 2) remain discriminative between treatment interventions. Extensive evaluations on real-world dataset are conducted, and results validate the effectiveness of the proposed framework.
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spelling pubmed-86542422021-12-09 Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases Ren, Weijeiying Liu, Kunpeng Zhao , Tianxiang Fu , Yanjie Front Big Data Big Data An accurate crime prediction and risk estimation can help improve the efficiency and effectiveness of policing activities. However, reports have revealed that biases like racial prejudice could exist in policing enforcement, and trained predictors may inherit them. In this work, we study the possible reasons and countermeasures to this problem, using records from the New York frisk and search program (NYCSF) as the dataset. Concretely, we provide analysis on the possible origin of this phenomenon from the perspective of risk discrepancy, and study it with the scope of selection bias. Motivated by theories in causal inference, we propose a re-weighting approach based on propensity score to balance the data distribution, with respect to the identified treatment: search action. Naively applying existing re-weighting approaches in causal inference is not suitable as the weight is passively estimated from observational data. Inspired by adversarial learning techniques, we formulate the predictor training and re-weighting as a min-max game, so that the re-weighting scale can be automatically learned. Specifically, the proposed approach aims to train a model that: 1) able to balance the data distribution in the searched and un-searched groups; 2) remain discriminative between treatment interventions. Extensive evaluations on real-world dataset are conducted, and results validate the effectiveness of the proposed framework. Frontiers Media S.A. 2021-11-24 /pmc/articles/PMC8654242/ /pubmed/34901844 http://dx.doi.org/10.3389/fdata.2021.787459 Text en Copyright © 2021 Ren, Liu, Zhao  and Fu . https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Ren, Weijeiying
Liu, Kunpeng
Zhao , Tianxiang
Fu , Yanjie
Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases
title Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases
title_full Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases
title_fullStr Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases
title_full_unstemmed Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases
title_short Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases
title_sort fair and effective policing for neighborhood safety: understanding and overcoming selection biases
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654242/
https://www.ncbi.nlm.nih.gov/pubmed/34901844
http://dx.doi.org/10.3389/fdata.2021.787459
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