fair data: a new research project

Happy to have been awarded funding through a highly competitive Strategic Research & Impact Programme (SRIP) call at UWE to co-lead a research project on "Fair Data: How to Implement Fairness in Trusted Research Environments." Even happier to extend my long-lasting collaboration with razor-sharp Lizzie Green and Kyle Alves and other UWE Dragon friends.

​Trusted Research Environments (TREs) are secure infrastructures that allow approved researchers to access sensitive data while protecting privacy and confidentiality. As TREs become central to data-driven research in areas such as public health and policy, questions about their ethical governance have grown increasingly urgent. While existing frameworks focus primarily on security and trust, a growing body of scholarship argues that these principles alone are insufficient to ensure the legitimacy of data governance systems. A recent example of the failure of such systems is the UK Biobank leak, which demonstrated that claims of operating a secure server are not sufficient, following reports that health data from hundreds of thousands of participants appeared for sale online and on public code-sharing platforms.

This project builds directly on our recently published interdisciplinary study (Tava, Alves, and Green, 2025), proposing a fairness model for TRE governance grounded in public benefit. Drawing on the concept of fairness, the model integrates both defensive motivations, such as non-discrimination and redistribution, and progressive ones, including transparency, participation, and data agency. The article concluded with four concrete policy and practice recommendations for TRE operators and data practitioners, and explicitly identified the collection of expert feedback on the model's practical applicability as the essential next step for research in this area.

This project takes that step. We will engage a targeted group of practitioners including data managers, gatekeepers, ethics committee members, and data scientists through workshops and structured interviews, to empirically test the TRE fairness model, refine it in response to their expertise, and document measurable changes in professional practice.
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The project has a dual Research and Impact focus. It will generate new empirical knowledge about operationalising fairness in data governance, while equipping practitioners with a tested, usable framework that meaningfully improves how they work.