Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality
By M. Oswald, Jamie Grace, S. Urwin, G. Barnes. 2018
176 citations recorded by the source corpus.
ABSTRACT 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.
Subfields: Law, Political Science and International Relations, Safety Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Law
In Political Science and International Relations
In Safety Research
Citations in the corpus
- Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies (2021, background): “Some promising approaches developed specifically for AI include the IEEE 7010 (2020) Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-being, AI Now’s Algorithmic Impact Assessment (Reisman et al., 2018); ALGO-CARE (Oswald et al., 2018), and Women Lead”
- Algorithmic content moderation: Technical and political challenges in the automation of platform governance (2020, background): “Much like in other areas, such as risk scoring (Oswald et al., 2018), automated systems for moderation introduce significant complexity that poses a challenge from an auditing perspective.”
- Interpretable Machine Learning (2019, background): “Human rights law and administrative law may also apply to certain ML applications.(25,69,70) ML-based decisions must also comply with the Equality Act 2010, which prohibits certain kinds of discrimination based on protected characteristics.”