Making harm visible: survivor-centred analysis of domestic homicide reviews using AI
Domestic homicide reviews are official reports in the UK written after a person is killed by a partner or family member to understand what went wrong and what actions could prevent future deaths. They should turn a fatal abuse case into lessons for prevention. However, these reviews are often lengthy, time and resource-intensive, and result in vague or broad agency recommendations, usually at the local level.
Police forces and review panels are now exploring how to use AI large language models for these reviews, but it is unclear whether this digital turn will enhance or erode equity, accountability, and victim voice, which should be central to domestic homicide review processes.
This project investigates how AI large language models are and will be used to write and analyse domestic homicide reviews, improving transparency, equity, and organisational learning. Specifically, the project will work with survivors to scrutinise whether, and under what conditions, large language models can be built and evaluated as a ‘digital good’ when drafting and analysing domestic homicide reviews.
Findings will inform strategies for the responsible deployment of AI to support: a) the analysis of domestic homicide reviews, improving the recognition of harm that may be more hidden, such as in cases of coercive control; and b) the writing of DHRs, helping agencies, review boards, and courts produce more timely, actionable, and survivor-centred recommendations.
Team
Kelly Bracewell – Senior Research Fellow, University of Lancashire
Gabriele Pergola – Assistant Professor in Natural Language Processing, University of Warwick
Khatidja Chantler – Professor of Gender, Equalities & Communities, Manchester Metropolitan University
Andy Myhill – Evidence & Evaluation Advisor, College of Policing
Katharine Hoeger – Senior Research Fellow, College of Policing & Research Associate, University of Oxford
Shelley Wilson – R&D Lead & Scientific Officer, Forensic Capability Network