Guidelines and Resources for AI Model Access from TrusTEd Researchenvironments (GRAIMatter)

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Trusted Research Environments (TREs) provide a secure location for researchers to analyse data for projects in the public interest e.g. providing information to SAGE to fight the COVID-19 pandemic. TRE staff check outputs to prevent disclosure of individuals’ confidential data.TREs have historically supported only classical statistical data analysis. There is an increasing need to also facilitate the training of Artificial Intelligence (AI) models. AI has many valuable applications e.g., spotting human errors, streamlining processes, helping with repetitive tasks and supporting clinical decision making. The trained models then need to be exported from TREs for use. The size and complexity of AI models presents significant challenges for the disclosure-checking process. Models may be susceptible to external hacking: complicated methods to reverse engineer the learning process to find out about the data used for training, with more potential to lead to re-identification than conventional statistical methods.With input from public representatives, GRAIMatter will assess a range of tools and methods to support TREs to assess output from AI methods for potentially identifiable information, investigate the legal and ethical implications and controls, and produce a set of guidelines and recommendations to support all TREs with export controls of AI algorithms.