Introduction

The use of data-driven technologies within the criminal justice system raises a host of challenging ethical issues, such as how to ensure automated decisions are explainable, or how to design governance processes to promote ethical values such as reliability, accountability, and fairness. Working in close collaboration with the Ministry of Justice and various stakeholders, this project will co-develop an ethical framework and toolkit that is tailored to the unique demands of the criminal justice system.

Explaining the science

The theoretical foundation for this project is a practically-oriented framework of ethical values and principles that grounds the development and use of data science and AI, and supports an end-to-end, procedural approach to the governance of data-driven technologies that aims to promote responsible research and innovation.

Each component imparts distinct, yet complementary methodological and conceptual perspectives to guide the research throughout this project. On the one hand, the ethical values are designed to support, underwrite, and motivate ethical conversations within and between teams (and organisations) by providing an accessible, common ground for thinking about the moral scope of the societal and ethical impacts of data-driven technologies. On the other hand, the ethical principles provide actionable and operationalisable points of departure to help teams reflect upon and justify why the actions they have taken throughout their project are, for example, bias-mitigating, non-discriminatory, and fair.

To ensure that the research and innovation life-cycle delivers on these goals, however, also requires an awareness of the governance processes within the organisation that oversee the design, development, and deployment of data-driven technologies. For instance, ensuring that AI-enabled decision-making systems support the delivery of interpretable outcomes and explainable processes (i.e. promoting ethical principles such as 'accountability' and 'explainability') requires careful consideration of both social and technical factors, such as who are the end-users of the technology and how will the technology affect their daily practices. Answering and acting upon these sorts of questions will likely require changes to existing organisational processes. In other words, ensuring the efficacy of an ethical framework and toolkit requires an assessment of the organisation's readiness and capacity for acting upon the guidance.

Project aims

In June 2019, the Turing’s Public Policy Programme, together with the Office for AI (OAI) and the Government Digital Service (GDS), published ethical guidelines for the design and use of artificial intelligence systems by all public sector agencies of the United Kingdom. These guidelines received Ministerial approval, forming the basis of the OAI’s and GDS’s Guide to using AI in the public sector. The Public Policy Programme is now collaborating with the Ministry of Justice, using the aforementioned guidance as a foundation to help address the unique challenges of using data-driven technologies within the criminal justice system.

The main aim of this project is to co-produce an ethical framework and toolkit that can help stakeholders, developers, policy-makers, and decision-makers understand what is required to enable responsible research and innovation for the use of AI and Data Science within the criminal justice system. 

Achieving this goal requires an in-depth analysis of the context in which the data-driven technologies are designed, developed, and ultimately deployed. This contextual analysis includes scoping and mapping activities that allow the project team to identify and understand the organisational culture and structures within the criminal justice system; algorithmic impact assessments that identify possible risks associated with applications of data science or AI; and stakeholder engagement workshops to ensure that the research is open to and directed by inclusive visions of how emerging technologies should serve the public good. 

This project is supported entirely by public funds, through Wave 1 of the UK Research and Innovation Strategic Priorities Fund, under EPSRC Grant EP/T001569/1.

Organisers

Researchers and collaborators

Funders