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Algorithmic Decision-Making in Criminal Justice

Project Description

Criminal justice agencies have been relatively early adopters of artificial intelligence systems. Amongst the most ambitious — and potentially controversial — experiments have involved using data-driven algorithms to make decisions about remand, court rulings, and sentencing. It is possible to see this development as the logical extension of a process of standardisation that has been underway for several decades within criminal justice. At the same time, the introduction of algorithmic decision-making in criminal justice potentially signals a radical change for a system historically dependent on human discretion, deliberation, and adjudication. What happens to these core features of criminal justice, if and when algorithmic decision-making becomes the norm? And, thinking on from this, how does this shift transform the bases for accountability and public trust in criminal justice?

This PhD project will seek to answer these questions through an examination of the practical, ethical, and socio-legal implications of algorithmic decision-making in relation to remand, court rulings, and/or sentencing. This might involve an assessment of the risks, challenges, and potential gains in using algorithmic decision-making in these areas. It will also, as a necessity, require an analysis that places new technological innovations in historical and social context, so that the promise and function of algorithmic decision-making is seen in terms of broader institutional developments.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI) which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at:

The project will contribute to explanations of how AI affects the social world and it will use social scientific concepts to do so. Its empirical discussion will also contribute to computer science and engineering understandings of the ethical, political and social implications of machine-learning and/or automotive design in governmental practice in complex legislative environments.

Applicants are free to set out their own ideas for the study’s methodology, but it is expected that a case study approach will be taken — potentially comparative and cross-jurisdictional. Applicants from a range of disciplinary backgrounds are encouraged to apply, but a good grounding in sociology, socio-legal studies, and/or criminology will be necessary to undertake the project.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

In addition, it is anticipated that applicants will have a background in a social science related subject, such as sociology, politics and/or geography. They should have good quantitative skills to enable them to take full advantage of the interdisciplinary training. Prior knowledge and work on computer-based decision-making would be an advantage, but is not essential. Students will receive training tailored to their background and project. This may include: programming, digital data and advanced quantitative methods (social statistics), AI ethics, AI and government.

Informal enquiries about the project should be directed to Dr Emma Carmel on email address .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science:

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

How good is research at University of Bath in Social Work and Social Policy?

FTE Category A staff submitted: 35.55

Research output data provided by the Research Excellence Framework (REF)

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