Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality
Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality
复制标题
算法风险评估警务模型：Durham HART 模型和“实验”比例性的经验教训
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
G. Barnes
中科院分区：
文献类型：
--
作者：
M. Oswald;Jamie Grace;S. Urwin;G. Barnes
摘要与整个公共部门一样，英国警察部门面临着压力，要求他们少花钱多办事，更有效地利用资源，并采取措施主动识别威胁;例如，根据“克莱尔法”和“萨拉法”等风险评估计划。通过更好地利用来自警察内部和外部的数据（包括情报），警务工具有望提高警察部队的决策和预测能力。本文使用达勒姆康斯特朗的危害评估风险工具（哈特）作为案例研究。哈特是第一个算法模型部署的英国警察部队在业务能力。我们的文章评论了这些工具的潜在好处，解释了哈特的概念和方法，并考虑了模型的使用和准确性的第一次验证的结果。然后，文章从社会和法律的角度批评了警务中算法工具的使用，特别关注司法审查的实质性普通法理由。它考虑了一个“实验”比例的概念，允许在公共部门以受控和有时限的方式使用未经验证的算法，并作为打击算法不透明性的方法组合的一部分，提出了“ALGO-CARE”，这是一个指导框架，涉及警方使用算法风险评估工具时应考虑的一些关键法律的和实际问题。文章的结论是，为了在警务环境中使用算法工具，以产生“更好”的结果，也就是说，在更一致，基于证据的决策环境中更有效地利用警察资源，那么应该开发一种“实验性”的比例方法，以确保可以为传统上由云计算产生的刑事司法问题找到“大数据”的新解决方案，非增广决策。最后，这篇文章指出，有一个子集的决定，周围有太大的影响，对社会和个人的福利，他们受到新兴技术的影响;在某种程度上，事实上，他们应该从算法决策的影响完全删除。
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.