GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)
Abstract
TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure and transparent research whilst protecting data confidentiality. There is an increasing desire from academia and industry to train AI models in TREs. The field of AI is developing quickly with applications including spotting human errors, streamlining processes, task automation and decision support. These complex AI models require more information to describe and reproduce, increasing the possibility that sensitive personal data can be inferred from such descriptions. TREs do not have mature processes and controls against these risks. This is a complex topic, and it is unreasonable to expect all TREs to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training. GRAIMATTER has developed a draft set of usable recommendations for TREs to guard against the additional risks when disclosing trained AI models from TREs. The development of these recommendations has been funded by the GRAIMATTER UKRI DARE UK sprint research project. This version of our recommendations was published at the end of the project in September 2022. During the course of the project, we have identified many areas for future investigations to expand and test these recommendations in practice. Therefore, we expect that this document will evolve over time.
Keywords
Cite
@article{arxiv.2211.01656,
title = {GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)},
author = {Emily Jefferson and James Liley and Maeve Malone and Smarti Reel and Alba Crespi-Boixader and Xaroula Kerasidou and Francesco Tava and Andrew McCarthy and Richard Preen and Alberto Blanco-Justicia and Esma Mansouri-Benssassi and Josep Domingo-Ferrer and Jillian Beggs and Antony Chuter and Christian Cole and Felix Ritchie and Angela Daly and Simon Rogers and Jim Smith},
journal= {arXiv preprint arXiv:2211.01656},
year = {2022}
}