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Machine learning models in trusted research environments - Understanding operational risks

Ritchie, Felix; Tilbrook, Amy; Cole, Christian; Jefferson, Emily; Krueger, Susan; Mansouri-Benssassi, Esma; Rogers, Simon; Smith, Jim

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Authors

Amy Tilbrook

Christian Cole

Emily Jefferson

Susan Krueger

Esma Mansouri-Benssassi

Simon Rogers

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Jim Smith James.Smith@uwe.ac.uk
Professor in Interactive Artificial Intelligence



Abstract

IntroductionTrusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amount of data; if this data is personal, the TRE is a well-established data management solution. However, ML models present novel disclosure risks, in both type and scale.ObjectivesAs part of a series on ML disclosure risk in TREs, this article is intended to introduce TRE managers to the conceptual problems and work being done to address them.MethodsWe demonstrate how ML models present a qualitatively different type of disclosure risk, compared to traditional statistical outputs. These arise from both the nature and the scale of ML modelling.ResultsWe show that there are a large number of unresolved issues, although there is progress in many areas. We show where areas of uncertainty remain, as well as remedial responses available to TREs.ConclusionsAt this stage, disclosure checking of ML models is very much a specialist activity. However, TRE managers need a basic awareness of the potential risk in ML models to enable them to make sensible decisions on using TREs for ML model development.

Citation

Ritchie, F., Tilbrook, A., Cole, C., Jefferson, E., Krueger, S., Mansouri-Benssassi, E., …Smith, J. (2023). Machine learning models in trusted research environments - Understanding operational risks. International Journal of Population Data Science, 8(1), Article 2165. https://doi.org/10.23889/ijpds.v8i1.2165

Journal Article Type Article
Acceptance Date Oct 30, 2023
Online Publication Date Dec 14, 2023
Publication Date Dec 14, 2023
Deposit Date Oct 31, 2023
Publicly Available Date Jan 3, 2024
Journal International Journal of Population Data Science
Electronic ISSN 2399-4908
Publisher Swansea University
Peer Reviewed Peer Reviewed
Volume 8
Issue 1
Article Number 2165
DOI https://doi.org/10.23889/ijpds.v8i1.2165
Keywords Artificial intelligence, Confidentiality, Machine Learning, Data Enclave, Trusted Research Environment, Output Checking, Disclosure
Public URL https://uwe-repository.worktribe.com/output/11404305
PMID 38414545

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