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Published in: Artificial Intelligence and Law 1/2022

15-04-2021 | Review Article

A review of predictive policing from the perspective of fairness

Authors: Kiana Alikhademi, Emma Drobina, Diandra Prioleau, Brianna Richardson, Duncan Purves, Juan E. Gilbert

Published in: Artificial Intelligence and Law | Issue 1/2022

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Abstract

Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While there is much research on fair machine learning in general, there is a need to investigate fair machine learning techniques as they pertain to the predictive policing. Therefore, we evaluate the existing publications in the field of fairness in machine learning and predictive policing to arrive at a set of standards for fair predictive policing. We also review the evaluations of ML applications in the area of criminal justice and potential techniques to improve these technologies going forward. We urge that the growing literature on fairness in ML be brought into conversation with the legal and social science concerns being raised about predictive policing. Lastly, in any area, including predictive policing, the pros and cons of the technology need to be evaluated holistically to determine whether and how the technology should be used in policing.

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Literature
go back to reference Abdollahi B, Nasraoui O (2018) Transparency in fair machine learning: the case of explainable recommender systems. In: Human and Machine Learning. Springer, pp 21–35 Abdollahi B, Nasraoui O (2018) Transparency in fair machine learning: the case of explainable recommender systems. In: Human and Machine Learning. Springer, pp 21–35
go back to reference Altman M, Wood A, Vayena E (2018) A harm-reduction framework for algorithmic fairness. IEEE Secur Privacy 16(3):34–45CrossRef Altman M, Wood A, Vayena E (2018) A harm-reduction framework for algorithmic fairness. IEEE Secur Privacy 16(3):34–45CrossRef
go back to reference Asaro PM (2019) Ai ethics in predictive policing: from models of threat to an ethics of care. IEEE Technol Soc Mag 38(2):40–53MathSciNetCrossRef Asaro PM (2019) Ai ethics in predictive policing: from models of threat to an ethics of care. IEEE Technol Soc Mag 38(2):40–53MathSciNetCrossRef
go back to reference Bakke E (2018) Predictive policing: the argument for public transparency. NYU Ann Surv Am L 74:131 Bakke E (2018) Predictive policing: the argument for public transparency. NYU Ann Surv Am L 74:131
go back to reference Bellamy RK, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, Lohia P, Martino J, Mehta S, Mojsilovic A, et al. (2018) Ai fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv:181001943 Bellamy RK, Dey K, Hind M, Hoffman SC, Houde S, Kannan K, Lohia P, Martino J, Mehta S, Mojsilovic A, et al. (2018) Ai fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv:​181001943
go back to reference Benthall S, Haynes BD (2019) Racial categories in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp 289–298 Benthall S, Haynes BD (2019) Racial categories in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp 289–298
go back to reference Berk R, Heidari H, Jabbari S, Kearns M, Roth A (2018) Fairness in criminal justice risk assessments: the state of the art. Sociol Methods Res 0049124118782533 Berk R, Heidari H, Jabbari S, Kearns M, Roth A (2018) Fairness in criminal justice risk assessments: the state of the art. Sociol Methods Res 0049124118782533
go back to reference Binns R (2018) What can political philosophy teach us about algorithmic fairness? IEEE Secur Privacy 16(3):73–80CrossRef Binns R (2018) What can political philosophy teach us about algorithmic fairness? IEEE Secur Privacy 16(3):73–80CrossRef
go back to reference Brantingham PJ, Valasik M, Mohler GO (2018) Does predictive policing lead to biased arrests? Results from a randomized controlled trial. Stat Public Policy 5(1):1–6CrossRef Brantingham PJ, Valasik M, Mohler GO (2018) Does predictive policing lead to biased arrests? Results from a randomized controlled trial. Stat Public Policy 5(1):1–6CrossRef
go back to reference Calmon FP, Wei D, Ramamurthy KN, Varshney KR (2017) Optimized data pre-processing for discrimination prevention. arXiv:170403354 Calmon FP, Wei D, Ramamurthy KN, Varshney KR (2017) Optimized data pre-processing for discrimination prevention. arXiv:​170403354
go back to reference Campedelli GM (2019) Where are we? Using scopus to map the literature at the intersection between artificial intelligence and crime. arXiv:1912.11084 Campedelli GM (2019) Where are we? Using scopus to map the literature at the intersection between artificial intelligence and crime. arXiv:​1912.​11084
go back to reference Chouldechova A (2017) Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2):153–163CrossRef Chouldechova A (2017) Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2):153–163CrossRef
go back to reference Corbett-Davies S, Goel S (2018) The measure and mismeasure of fairness: a critical review of fair machine learning. arXiv:180800023 Corbett-Davies S, Goel S (2018) The measure and mismeasure of fairness: a critical review of fair machine learning. arXiv:​180800023
go back to reference Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 797–806 Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A (2017) Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 797–806
go back to reference Degeling M, Berendt B (2018) What is wrong about robocops as consultants? A technology-centric critique of predictive policing. AI & Soc 33(3):347–356CrossRef Degeling M, Berendt B (2018) What is wrong about robocops as consultants? A technology-centric critique of predictive policing. AI & Soc 33(3):347–356CrossRef
go back to reference Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214–226 Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214–226
go back to reference Ensign D, Friedler SA, Neville S, Scheidegger C, Venkatasubramanian S (2017) Runaway feedback loops in predictive policing. arXiv:1706.09847 Ensign D, Friedler SA, Neville S, Scheidegger C, Venkatasubramanian S (2017) Runaway feedback loops in predictive policing. arXiv:​1706.​09847
go back to reference Ferguson AG (2016) Policing predictive policing. Wash UL Rev 94:1109 Ferguson AG (2016) Policing predictive policing. Wash UL Rev 94:1109
go back to reference Friedler SA, Scheidegger C, Venkatasubramanian S, Choudhary S, Hamilton EP, Roth D (2019) A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the conference on fairness, accountability, and transparency, pp 329–338 Friedler SA, Scheidegger C, Venkatasubramanian S, Choudhary S, Hamilton EP, Roth D (2019) A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the conference on fairness, accountability, and transparency, pp 329–338
go back to reference Garvie C (2016) The perpetual line-up: unregulated police face recognition in America. Georgetown Law, Center on Privacy & Technology Garvie C (2016) The perpetual line-up: unregulated police face recognition in America. Georgetown Law, Center on Privacy & Technology
go back to reference Grgic-Hlaca N, Zafar MB, Gummadi KP, Weller A (2016) The case for process fairness in learning: feature selection for fair decision making. In: NIPS symposium on machine learning and the law, vol 1, p 2 Grgic-Hlaca N, Zafar MB, Gummadi KP, Weller A (2016) The case for process fairness in learning: feature selection for fair decision making. In: NIPS symposium on machine learning and the law, vol 1, p 2
go back to reference Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. In: Advances in neural information processing systems, pp 3315–3323 Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. In: Advances in neural information processing systems, pp 3315–3323
go back to reference Heidari H, Loi M, Gummadi KP, Krause A (2019) A moral framework for understanding fair ml through economic models of equality of opportunity. In: Proceedings of the conference on fairness, accountability, and transparency, pp 181–190 Heidari H, Loi M, Gummadi KP, Krause A (2019) A moral framework for understanding fair ml through economic models of equality of opportunity. In: Proceedings of the conference on fairness, accountability, and transparency, pp 181–190
go back to reference Joh EE (2017) Artificial intelligence and policing: first questions. Seattle UL Rev 41:1139 Joh EE (2017) Artificial intelligence and policing: first questions. Seattle UL Rev 41:1139
go back to reference Kusner MJ, Loftus J, Russell C, Silva R (2017) Counterfactual fairness. In: Advances in neural information processing systems, pp 4066–4076 Kusner MJ, Loftus J, Russell C, Silva R (2017) Counterfactual fairness. In: Advances in neural information processing systems, pp 4066–4076
go back to reference Lohia PK, Ramamurthy KN, Bhide M, Saha D, Varshney KR, Puri R (2019) Bias mitigation post-processing for individual and group fairness. In: Icassp 2019–2019 ieee international conference on acoustics, speech and signal processing (icassp), IEEE, pp 2847–2851 Lohia PK, Ramamurthy KN, Bhide M, Saha D, Varshney KR, Puri R (2019) Bias mitigation post-processing for individual and group fairness. In: Icassp 2019–2019 ieee international conference on acoustics, speech and signal processing (icassp), IEEE, pp 2847–2851
go back to reference Lum K, Isaac W (2016a) Predictive policing reinforces police bias. Human Rights Data Anal Group Lum K, Isaac W (2016a) Predictive policing reinforces police bias. Human Rights Data Anal Group
go back to reference Marda V, Narayan S (2020) Data in new delhi’s predictive policing system. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 317–324 Marda V, Narayan S (2020) Data in new delhi’s predictive policing system. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 317–324
go back to reference Martinez N, Bertran M, Sapiro G (2019) Fairness with minimal harm: a pareto-optimal approach for healthcare. arXiv:191106935 Martinez N, Bertran M, Sapiro G (2019) Fairness with minimal harm: a pareto-optimal approach for healthcare. arXiv:​191106935
go back to reference Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2019) A survey on bias and fairness in machine learning. arXiv:190809635 Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2019) A survey on bias and fairness in machine learning. arXiv:​190809635
go back to reference Mohler GO, Short MB, Malinowski S, Johnson M, Tita GE, Bertozzi AL, Brantingham PJ (2015) Randomized controlled field trials of predictive policing. J Am Stat Assoc 110(512):1399–1411MathSciNetCrossRef Mohler GO, Short MB, Malinowski S, Johnson M, Tita GE, Bertozzi AL, Brantingham PJ (2015) Randomized controlled field trials of predictive policing. J Am Stat Assoc 110(512):1399–1411MathSciNetCrossRef
go back to reference Nissan E (2017) Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement. Ai & Soc 32(3):441–464MathSciNetCrossRef Nissan E (2017) Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement. Ai & Soc 32(3):441–464MathSciNetCrossRef
go back to reference Perrot P (2017) What about ai in criminal intelligence? From predictive policing to ai perspectives. Eur Law Enforc Res Bull 16:65–75 Perrot P (2017) What about ai in criminal intelligence? From predictive policing to ai perspectives. Eur Law Enforc Res Bull 16:65–75
go back to reference Perry W, McInnis B, Price C, Smith S, Hollywood J (2018) Predictive Policing: the role of crime forecasting in law enforcement operations. RAND Corporation, Tech. rep Perry W, McInnis B, Price C, Smith S, Hollywood J (2018) Predictive Policing: the role of crime forecasting in law enforcement operations. RAND Corporation, Tech. rep
go back to reference Perry WL (2013) Predictive policing: the role of crime forecasting in law enforcement operations. Rand Corporation, Santa MonicaCrossRef Perry WL (2013) Predictive policing: the role of crime forecasting in law enforcement operations. Rand Corporation, Santa MonicaCrossRef
go back to reference Persson A, Kavathatzopoulos I (2018a) How to make decisions with algorithms. ACM SIGCAS Comput Soc 47(4):122–133CrossRef Persson A, Kavathatzopoulos I (2018a) How to make decisions with algorithms. ACM SIGCAS Comput Soc 47(4):122–133CrossRef
go back to reference Persson A, Kavathatzopoulos I (2018b) How to make decisions with algorithms: ethical decision-making using algorithms within predictive analytics. ACM SIGCAS Comput Soc 47(4):122–133CrossRef Persson A, Kavathatzopoulos I (2018b) How to make decisions with algorithms: ethical decision-making using algorithms within predictive analytics. ACM SIGCAS Comput Soc 47(4):122–133CrossRef
go back to reference Reisman D, Schultz J, Crawford K, Whittaker M (2018) Algorithmic impact assessments: a practical framework for public agency accountability. Tech. rep., AI Now Institute Reisman D, Schultz J, Crawford K, Whittaker M (2018) Algorithmic impact assessments: a practical framework for public agency accountability. Tech. rep., AI Now Institute
go back to reference Richardson R, Schultz J, Crawford K (2019) Dirty data, bad predictions: how civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review Online, Forthcoming Richardson R, Schultz J, Crawford K (2019) Dirty data, bad predictions: how civil rights violations impact police data, predictive policing systems, and justice. New York University Law Review Online, Forthcoming
go back to reference Ridgeway G (2013) The pitfalls of preduction. NIJ J 271:34–40 Ridgeway G (2013) The pitfalls of preduction. NIJ J 271:34–40
go back to reference Saleiro P, Kuester B, Hinkson L, London J, Stevens A, Anisfeld A, Rodolfa KT, Ghani R (2018) Aequitas: a bias and fairness audit toolkit. arXiv:181105577 Saleiro P, Kuester B, Hinkson L, London J, Stevens A, Anisfeld A, Rodolfa KT, Ghani R (2018) Aequitas: a bias and fairness audit toolkit. arXiv:​181105577
go back to reference Santos RB (2019) Predictive policing: where’s the evidence? In: Police innovation: contrasting perspectives. Cambridge University Press, p 366 Santos RB (2019) Predictive policing: where’s the evidence? In: Police innovation: contrasting perspectives. Cambridge University Press, p 366
go back to reference Selbst AD (2017) Disparate impact in big data policing. Ga L Rev 52:109 Selbst AD (2017) Disparate impact in big data policing. Ga L Rev 52:109
go back to reference Shrestha YR, Yang Y (2019) Fairness in algorithmic decision-making: applications in multi-winner voting, machine learning, and recommender systems. Algorithms 12(9):199MathSciNetCrossRef Shrestha YR, Yang Y (2019) Fairness in algorithmic decision-making: applications in multi-winner voting, machine learning, and recommender systems. Algorithms 12(9):199MathSciNetCrossRef
go back to reference Speicher T, Heidari H, Grgic-Hlaca N, Gummadi KP, Singla A, Weller A, Zafar MB (2018) A unified approach to quantifying algorithmic unfairness: measuring individual&group unfairness via inequality indices. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2239–2248 Speicher T, Heidari H, Grgic-Hlaca N, Gummadi KP, Singla A, Weller A, Zafar MB (2018) A unified approach to quantifying algorithmic unfairness: measuring individual&group unfairness via inequality indices. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2239–2248
go back to reference Verma S, Rubin J (2018) Fairness definitions explained. In: 2018 IEEE/ACM international workshop on software fairness (FairWare), IEEE, pp 1–7 Verma S, Rubin J (2018) Fairness definitions explained. In: 2018 IEEE/ACM international workshop on software fairness (FairWare), IEEE, pp 1–7
go back to reference Vestby A, Vestby J (2019) Machine learning and the police: asking the right questions. Policing J Policy Pract Vestby A, Vestby J (2019) Machine learning and the police: asking the right questions. Policing J Policy Pract
go back to reference Wang H, Grgic-Hlaca N, Lahoti P, Gummadi KP, Weller A (2019) An empirical study on learning fairness metrics for compas data with human supervision. arXiv:1910.10255 Wang H, Grgic-Hlaca N, Lahoti P, Gummadi KP, Weller A (2019) An empirical study on learning fairness metrics for compas data with human supervision. arXiv:​1910.​10255
go back to reference Wexler J, Pushkarna M, Bolukbasi T, Wattenberg M, Viégas F, Wilson J (2019) The what-if tool: interactive probing of machine learning models. IEEE Trans Visual Comput Graph 26(1):56–65 Wexler J, Pushkarna M, Bolukbasi T, Wattenberg M, Viégas F, Wilson J (2019) The what-if tool: interactive probing of machine learning models. IEEE Trans Visual Comput Graph 26(1):56–65
Metadata
Title
A review of predictive policing from the perspective of fairness
Authors
Kiana Alikhademi
Emma Drobina
Diandra Prioleau
Brianna Richardson
Duncan Purves
Juan E. Gilbert
Publication date
15-04-2021
Publisher
Springer Netherlands
Published in
Artificial Intelligence and Law / Issue 1/2022
Print ISSN: 0924-8463
Electronic ISSN: 1572-8382
DOI
https://doi.org/10.1007/s10506-021-09286-4

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