Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.

Introduction: Artificial intelligence (AI) applications in healthcare and medicine have increased in recent years. To enable access to personal data, Trusted Research Environments (TREs) (otherwise known as Safe Havens) provide safe and secure environments in which researchers can access sensitive personal data and develop AI (in particular machine learning (ML)) models. However, currently few TREs support the training of ML models in part due to a gap in the practical decision-making guidance for TREs in handling model disclosure. Specifically, the training of ML models creates a need to disclose new types of outputs from TREs. Although TREs have clear policies for the disclosure of statistical outputs, the extent to which trained models can leak personal training data once released is not well understood.
Background: We review, for a general audience, different types of ML models and their applicability within healthcare. We explain the outputs from training a ML model and how trained ML models can be vulnerable to external attacks to discover personal data encoded within the model.
Risks: We present the challenges for disclosure control of trained ML models in the context of training and exporting models from TREs. We provide insights and analyse methods that could be introduced within TREs to mitigate the risk of privacy breaches when disclosing trained models.
Discussion: Although specific guidelines and policies exist for statistical disclosure controls in TREs, they do not satisfactorily address these new types of output requests; i.e., trained ML models. There is significant potential for new interdisciplinary research opportunities in developing and adapting policies and tools for safely disclosing ML outputs from TREs.
Competing Interests: The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Dr Esma Mansouri-Benssassi, Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, Scotland. Dr Simon Rogers, Artificial Intelligence Centre of Excellence, NHS National Services Scotland, Glasgow, Scotland. Dr Smarti Reel, Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, Scotland. Maeve Malone, School of Humanities Social Sciences and Law, University of Dundee, Scotland. Prof Jim Smith, School of Computer Science and Creative Technologies, University of West England, Bristol, England. Prof Felix Ritchie, FBL - Accounting, Economics and Finance, University of West England, Bristol, England. Prof Emily Jefferson, Division of Population Health and Genomics, School of Medicine, University of Dundee & HDR UK.
(© 2023 The Authors.)