Dr Daniel Marciniak D.F.Marciniak@hull.ac.uk
Lecturer
Predictive policing has captured the imagination of both enthusiasts hoping to improve public safety and opponents raising concerns around algorithmic bias and opacity. Based on seven in-depth interviews with officers in a UK police force, this article examines the dynamics of how automated risk scores institutionalise an individual-focussed threat-harm-risk strategy aimed at preventing repeat offending. Born out of the need to prioritise work given budget cuts, the risk scores alleviate fears of missing opportunities for prevention and render professional decision-making defendable. Rather than replacing professional judgement, the article finds that officers maintain discretion in a process of co-construction by scrutinising the risk scores and weighing them against other priorities and operational constraints. In a climate of austerity, a concern arises from the scores’ potential to drive short-term selective incapacitation rather than prevention through supportive measures.
Marciniak, D. (2022). Algorithmic Policing: An exploratory study of the algorithmically mediated construction of individual risk in a UK police force. Policing and Society, https://doi.org/10.1080/10439463.2022.2144305
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 31, 2022 |
Online Publication Date | Dec 15, 2022 |
Publication Date | 2022 |
Deposit Date | Dec 15, 2022 |
Publicly Available Date | Dec 15, 2022 |
Journal | Policing and Society |
Print ISSN | 1043-9463 |
Publisher | Routledge |
Peer Reviewed | Peer Reviewed |
DOI | https://doi.org/10.1080/10439463.2022.2144305 |
Keywords | Predictive policing; Risk; Discretion; Recidivism |
Public URL | https://hull-repository.worktribe.com/output/4116722 |
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Copyright Statement
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis GroupThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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