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:	Weijeiying Ren, Kunpeng Liu, Tianxiang Zhao , Yanjie Fu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	counterfactual learning neighborhood safety fairness stop-and-frisk adversarial learning Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/276b831788b747ab8623b025217025f2
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id	oai:doaj.org-article:276b831788b747ab8623b025217025f2
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spelling	oai:doaj.org-article:276b831788b747ab8623b025217025f22021-11-30T18:30:56ZFair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases2624-909X10.3389/fdata.2021.787459https://doaj.org/article/276b831788b747ab8623b025217025f22021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fdata.2021.787459/fullhttps://doaj.org/toc/2624-909XAn 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.Weijeiying RenKunpeng LiuTianxiang Zhao Yanjie Fu Frontiers Media S.A.articlecounterfactual learningneighborhood safetyfairnessstop-and-friskadversarial learningInformation technologyT58.5-58.64ENFrontiers in Big Data, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	counterfactual learning neighborhood safety fairness stop-and-frisk adversarial learning Information technology T58.5-58.64
spellingShingle	counterfactual learning neighborhood safety fairness stop-and-frisk adversarial learning Information technology T58.5-58.64 Weijeiying Ren Kunpeng Liu Tianxiang Zhao Yanjie Fu Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases
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.
format	article
author	Weijeiying Ren Kunpeng Liu Tianxiang Zhao Yanjie Fu
author_facet	Weijeiying Ren Kunpeng Liu Tianxiang Zhao Yanjie Fu
author_sort	Weijeiying Ren
title	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_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_sort	fair and effective policing for neighborhood safety: understanding and overcoming selection biases
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/276b831788b747ab8623b025217025f2
work_keys_str_mv	AT weijeiyingren fairandeffectivepolicingforneighborhoodsafetyunderstandingandovercomingselectionbiases AT kunpengliu fairandeffectivepolicingforneighborhoodsafetyunderstandingandovercomingselectionbiases AT tianxiangzhao fairandeffectivepolicingforneighborhoodsafetyunderstandingandovercomingselectionbiases AT yanjiefu fairandeffectivepolicingforneighborhoodsafetyunderstandingandovercomingselectionbiases
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Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases

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