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TREs are widely, and increasingly used to support statistical analysis ofsensitive data across a range of sectors (e.g., health, police, tax andeducation) as they enable secure and transparent research whilst protectingdata confidentiality. There is an increasing desire from academia and industryto train AI models in TREs. The field of AI is developing quickly withapplications including spotting human errors, streamlining processes, taskautomation and decision support. These complex AI models require moreinformation to describe and reproduce, increasing the possibility thatsensitive personal data can be inferred from such descriptions. TREs do nothave mature processes and controls against these risks. This is a complextopic, and it is unreasonable to expect all TREs to be aware of all risks orthat TRE researchers have addressed these risks in AI-specific training.GRAIMATTER has developed a draft set of usable recommendations for TREs toguard against the additional risks when disclosing trained AI models from TREs.The development of these recommendations has been funded by the GRAIMATTER UKRIDARE UK sprint research project. This version of our recommendations waspublished at the end of the project in September 2022. During the course of theproject, we have identified many areas for future investigations to expand andtest these recommendations in practice. Therefore, we expect that this documentwill evolve over time.

GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)