Data analytics and algorithms in policing in England and Wales: Towards a new policy framework

RUSI was commissioned by the Centre for Data Ethics and Innovation (CDEI) to conduct an independent study into the use of data analytics by police forces in England and Wales, with a focus on algorithmic bias. The primary purpose of the project is to inform CDEI’s review of bias in algorithmic decision-making, which is focusing on four sectors, including policing, and working towards a draft framework for the ethical development and deployment of data analytics tools for policing. This paper focuses on advanced algorithms used by the police to derive insights, inform operational decision-making or make predictions. Biometric technology, including live facial recognition, DNA analysis and fingerprint matching, are outside the direct scope of this study, as are covert surveillance capabilities and digital forensics technology, such as mobile phone data extraction and computer forensics. However, because many of the policy issues discussed in this paper stem from general underlying data protection and human rights frameworks, these issues will also be relevant to other police technologies, and their use must be considered in parallel to the tools examined in this paper. The project involved engaging closely with senior police officers, government officials, academics, legal experts, regulatory and oversight bodies and civil society organisations. Sixty nine participants took part in the research in the form of semi-structured interviews, focus groups and roundtable discussions. The project has revealed widespread concern across the UK law enforcement community regarding the lack of official national guidance for the use of algorithms in policing, with respondents suggesting that this gap should be addressed as a matter of urgency. Any future policy framework should be principles-based and complement existing police guidance in a ‘tech-agnostic’ way. Rather than establishing prescriptive rules and standards for different data technologies, the framework should establish standardised processes to ensure that data analytics projects follow recommended routes for the empirical evaluation of algorithms within their operational context and evaluate the project against legal requirements and ethical standards. The new guidance should focus on ensuring multi-disciplinary legal, ethical and operational input from the outset of a police technology project; a standard process for model development, testing and evaluation; a clear focus on the human–machine interaction and the ultimate interventions a data driven process may inform; and ongoing tracking and mitigation of discrimination risk

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

Data Analytics and Algorithmic Bias in Policing

This paper summarises the use of analytics and algorithms for policing within England and Wales, and explores different types of bias that can arise during the product lifecycle. The findings are based on in-depth consultation with police forces, civil society organisations, academics and legal experts. The primary purpose of the project is to inform the Centre for Data Ethics and Innovation's ongoing review into algorithmic bias in the policing sector. RUSI’s second and final report for this project will be published in early 2020, to include specific recommendations for the final Code of Practice, and incorporating the cumulative feedback received during the consultation process

Machine Learning Algorithms and Police Decision-Making: Legal, Ethical and Regulatory Challenges

This report seeks to critically assess the use of machine learning algorithms for policing, and provide practical recommendations designed to contribute to the fast-moving debate over policy and governance in this area

Behavioural Analytics in Policing Data-Driven Offender Management: Assessing the Evidence Base

This doctoral thesis explores the use of behavioural analytics and data-driven offender management within UK policing. The findings are based on semi-structured interviews with criminal justice practitioners who have been directly involved in the development of such projects, and a process evaluation of a major data-driven offender management project delivered by one of the UK’s largest police forces. The research explored the potential opportunities offered by new data-driven risk assessment tools, and sought to examine the barriers to successful implementation when the technology is deployed in an operational policing context. The process evaluation highlighted specific practical challenges associated with implementing a new data-driven system in an operational offender management context. The implications of these findings for future policing practice are explored, focussing on how such technology should be piloted and evaluated to assess its potential real-world benefits and limitations. The findings and discussion will be of interest to criminal justice practitioners and policymakers involved in developing or implementing new data-driven offender risk assessment tools in the UK criminal justice system

Machine learning predictive algorithms and the policing of future crimes: governance and oversight

This chapter focuses upon machine learning algorithms within police decision-making in England and Wales, specifically in relation to predictive analytics. It first reviews the state of the art regarding the implementation of algorithmic tools underpinned by machine learning to aid police decision-making, and notes the impact of austerity as a driver for the development of such tools. We discuss how what could be called ‘Austerity AI’ is often linked to the prevention and public protection common law duties and functions of the police, a broad and imprecise legal base that the ECtHR in Catt found less than satisfactory. The potential implications of these tools for appropriate application of discretion within policing, as well as their potential impact on individual rights are then considered. Finally, existing and recommended governance and oversight processes, including those designed to facilitate trials of emerging technologies, are reviewed, and proposals made for statutory clarification of policing functions and duties, thus providing a clearer framework against which proposals for new AI development can be assessed