RUDI: an evidence-based police-centric guide for approaching the development of algorithmic models in policing

Article

Sayer, H, Polajnar, T. and Spence, R. 2025. RUDI: an evidence-based police-centric guide for approaching the development of algorithmic models in policing. The Police Journal: Theory, Practice and Principles. https://doi.org/10.1177/0032258X251372357
TypeArticle
TitleRUDI: an evidence-based police-centric guide for approaching the development of algorithmic models in policing
AuthorsSayer, H, Polajnar, T. and Spence, R.
Abstract

Police are increasingly adopting or considering machine learning algorithms (MLAs) to enhance their processes, appealing for their ability to process large volumes of information and predictive capabilities. Lack of national guidance on developing and implementing algorithms means police forge ahead in an exploratory manner. To address this, we developed a practical police-centric framework and guide: RUDI (Rationale, Unification, Development, Implementation), a framework designed to mitigate concerns raised regarding the use of algorithms in policing. This report outlines our work with two police forces to develop RUDI, highlighting the challenges of algorithmic policing and how RUDI can mitigate these concerns.

Keywordsmachine learning; algorithms; framework; transparency; policing
Sustainable Development Goals16 Peace, justice and strong institutions
Middlesex University ThemeCreativity, Culture & Enterprise
Research GroupCentre for Abuse, Trauma and Suicide Studies (CATS)
PublisherSAGE Publications
JournalThe Police Journal: Theory, Practice and Principles
ISSN0032-258X
Electronic1740-5599
Publication dates
Online29 Aug 2025
Publication process dates
Submitted15 Oct 2024
Accepted13 Aug 2025
Deposited02 Sep 2025
Output statusPublished
Publisher's version
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File Access Level
Open
Copyright Statement

© The Author(s) 2025.
This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Digital Object Identifier (DOI)https://doi.org/10.1177/0032258X251372357
LanguageEnglish
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