A review of predictive policing from the perspective of fairness

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

doi:10.1007/s10506-021-09286-4

Cited by 167Ethics and Social Impacts of AIArtificial Intelligence in LawPolicing Practices and Perceptions

A review of predictive policing from the perspective of fairness

Alikhademi, Kiana, Drobina, Emma, Prioleau, Diandra, Richardson, Brianna, Purves, Duncan, Gilbert, Juan E.·2022·Artificial Intelligence and Law

DOI 10.1007/s10506-021-09286-4

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Utilizing AI fairness to address bias in risk assessment tools ensures that criminal sentencing decisions are fair.
“Utilizing AI fairness to address bias in risk assessment tools ensures that criminal sentencing decisions are fair [88,281].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [88]

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“Predictive policing involves using algorithms to analyse massive amounts of pre-existing crime data to identify times and locations, which have a high risk of crime, thus predicting and helping prevent the commission of potential future crimes (Alikhademi et al 2022; Degeling and Berendt 2017).”
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“Other authors have raised the issue of the scope and value that the predictions generated by such algorithms should receive in the police or judicial sphere (Hannah-Moffat 2009; Parmar and Freeman 2016; Ratcliffe 2019; Meijer and Wessels 2019; Garrett and Monahan 2020; Alikhademi et al. 2021; Ugwudike 2021).”
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“…abstract principles require a tremendous amount of work to operationalize, and they have led to disagreements at the technical level around what measures of success might be appropriate for judging, for example, the fairness of a model (Alikhademi et al. 2021) or its accountability (Wieringa 2020).”
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Data stratification analysis on the propagation of discriminatory effects in binary classification
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“As the use of predictive policing technology are becoming increasingly prevalent, the ethical concerns, such as fairness and privacy, of employing such models should also be care-fully studied and considered [64].”
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“…In ‘smart cities’, individuals are unwittingly spied upon by means of preventive AI technology and used for so-called ‘predictive policing’ (Alikhademi et al. 2021; de Menezes and Sanllehi 2021; Jahankhani et al. 2020; Micklitz et al. 2022; Završnik and Badalič 2021) and related…”
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Authors: Alikhademi, K., Drobina, E., Prioleau, D., Richardson, B., Purves, D., & Gilbert, J. E.
Affiliations: University of Florida (US)
Corresponding author: Alikhademi, Kiana
ORCID: Alikhademi, Kiana
Prioleau, Diandra
Richardson, Brianna
Purves, Duncan
Gilbert, Juan E.
Venue: Artificial Intelligence and Law
Publisher: Springer Science and Business Media LLC
Published: 2021-04-15
Volume: 30
Issue: 1
Pages: 1-17
ISSN: 0924-8463 (ISSN-L)
1572-8382
DOI: 10.1007/s10506-021-09286-4
OpenAlex: W3155016818
Cited by: 167
References: 53
Citations by year: 2026: 29 · 2025: 26 · 2024: 27 · 2023: 69 · 2022: 14 · 2021: 2
FWCI: 35.01
Type: journal article
Language: en
Update notices: correction (2021-08-28)
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Artificial Intelligence and Law (publishedVersion)
Funders: NSF (1917712)
Topics: Social Sciences › Safety Research › Ethics and Social Impacts of AI
Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
Keywords: predictive policing, algorithmic fairness, indirect discrimination, systemic bias, criminal justice, algorithmic bias, social justice

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