Algorithmic Risk Assessment Policing Models: Lessons from the Durham HART Model and ‘Experimental’ Proportionality

Information & Communications Technology Law, Forthcoming

27 Pages Posted: 5 Sep 2017 Last revised: 29 Nov 2017

See all articles by Marion Oswald

Marion Oswald

University of Northumbria at Newcastle; The Alan Turing Institute

Jamie Grace

Sheffield Hallam University

Sheena Urwin

Durham Constabulary

Geoffrey Barnes

University of Cambridge

Date Written: August 31, 2017


As is common across the public sector, the UK police service is under pressure to do more with less, to target resources more efficiently and take steps to identify threats proactively; for example under risk-assessment schemes such as ‘Clare’s Law’ and ‘Sarah’s Law’. Algorithmic tools promise to improve a police force’s decision-making and prediction abilities by making better use of data (including intelligence), both from inside and outside the force.

This article uses Durham Constabulary’s Harm Assessment Risk Tool (HART) as a case-study. HART is one of the first algorithmic models to be deployed by a UK police force in an operational capacity. Our article comments upon the potential benefits of such tools, explains the concept and method of HART and considers the results of the first validation of the model’s use and accuracy.

The article then critiques the use of algorithmic tools within policing from a societal and legal perspective, focusing in particular upon substantive common law grounds for judicial review. It considers a concept of ‘experimental’ proportionality to permit the use of unproven algorithms in the public sector in a controlled and time-limited way, and as part of a combination of approaches to combat algorithmic opacity, proposes ‘ALGO-CARE’, a guidance framework of some of the key legal and practical concerns that should be considered in relation to the use of algorithmic risk assessment tools by the police. The article concludes that for the use of algorithmic tools in a policing context to result in a ‘better’ outcome, that is to say, a more efficient use of police resources in a landscape of more consistent, evidence-based decision-making, then an ‘experimental’ proportionality approach should be developed to ensure that new solutions from ‘big data’ can be found for criminal justice problems traditionally arising from clouded, non-augmented decision-making. Finally, this article notes that there is a sub-set of decisions around which there is too great an impact upon society and upon the welfare of individuals for them to be influenced by an emerging technology; to an extent, in fact, that they should be removed from the influence of algorithmic decision-making altogether.

Keywords: Big Data, Algorithms, Machine Learning, Artificial Intelligence, Data Analytics, Policing, Crime, Black Box, Ethics, Transparency, Proportionality, Governance, Rule of Law

JEL Classification: K10, K14

Suggested Citation

Oswald, Marion and Grace, Jamie and Urwin, Sheena and Barnes, Geoffrey, Algorithmic Risk Assessment Policing Models: Lessons from the Durham HART Model and ‘Experimental’ Proportionality (August 31, 2017). Information & Communications Technology Law, Forthcoming, Available at SSRN: or

Marion Oswald (Contact Author)

University of Northumbria at Newcastle ( email )

Pandon Building
208, City Campus East-1
Newcastle-Upon-Tyne, Newcastle NE1 8ST
United Kingdom

The Alan Turing Institute ( email )

British Library
96 Euston Road
London, NW1 2DB
United Kingdom

Jamie Grace

Sheffield Hallam University ( email )

United Kingdom

Sheena Urwin

Durham Constabulary ( email )

United Kingdom

Geoffrey Barnes

University of Cambridge ( email )

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

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