GRAIMatter: Guidelines and Resources for AI Model Access from TrusTEd Research environments

Emily Jefferson, Christian Cole, Alba Crespi Boixader, Simon Rogers, Maeve Roche, Felix Ritchie, Jim Smith, Francesco Tava, Angela Daly, Jillian Beggs, Antony Chuter

Research output: Chapter in Book/Report/Conference proceedingConference contribution


To assess a range of tools and methods to support Trusted Research Environments (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.

TREs provide secure facilities to analyse confidential personal data, with staff checking outputs for disclosure risk before publication. Artificial intelligence (AI) has high potential to improve the linking and analysis of population data, and TREs are well suited to supporting AI modelling. However, TRE governance focuses on classical statistical data analysis. 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.

GRAIMatter is:

Quantitatively assessing the risk of disclosure from different AI models exploring different models, hyper-parameter settings and training algorithms over common data types

Evaluating a range of tools to determine effectiveness for disclosure control

Assessing the legal and ethical implications of TREs supporting AI development and identifying aspects of existing legal and regulatory frameworks requiring reform.

Running 4 PPIE workshops to understand their priorities and beliefs around safeguarding and securing data

Developing a set of recommendations including
suggested open-source toolsets for TREs to use to measure and reduce disclosure risk

descriptions of the technical and legal controls and policies TREs should implement across the 5 Safes to support AI algorithm disclosure control

training implications for both TRE staff and how they validate researchers

GRAIMatter is developing a set of usable recommendations for TREs to use to guard against the additional risks when disclosing trained AI models from TREs.
Original languageEnglish
Title of host publicationConference Proceedings for International Population Data Linkage Conference 2022
Publication statusPublished - 25 Aug 2022
EventInternational Population Data Linkage Conference 2022 - Edinburgh
Duration: 7 Sept 20229 Sept 2022
Conference number: 2022


ConferenceInternational Population Data Linkage Conference 2022
Abbreviated titleIPDLN
Internet address


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