Green Paper: Disclosure Control Of Al Algorithms From TREs (GRAIMATTER) lead: University of Dundee other instns: National Services Scotland, University of the West England UWE - Bristol, University of Edinburgh, URV (Spain)

Daly, Angela (Investigator)
Jefferson, Emily (Investigator)
Malone, Maeve (Investigator)

Status	Finished
Effective start/end date	4/01/22 → 31/08/22

Funding

Medical Research Council (MRC_PC_21033): £163,364.00

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

Health Informatics Centre
Health and Clinical Services
Facility/equipment: Facility

3 Public engagement and outreach - public lecture/debate/seminar
1 Public engagement and outreach - media article or participation
1 Invited talk
1 Oral presentation

Functional considerations of machine learning and personal data
Cole, C. (Speaker)
12 Dec 2023
Activity: Talk or presentation types › Oral presentation
Trusted Research Environments: defining a common specification and getting them ready for ML workflows
Cole, C. (Speaker)
8 Nov 2023
Activity: Talk or presentation types › Invited talk
GRAIMatter Public Workshop
Jefferson, E. (Member), Cole, C. (Member), Malone, M. (Member), Smith, J. (Member) & Rogers, S. (Member)
24 May 2022
Activity: Other activity types › Public engagement and outreach - public lecture/debate/seminar

2 Article
1 Other report
1 Conference contribution
1 Other contribution

Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare
Kerasidou, C. (Lead / Corresponding author), Malone, M., Daly, A. & Tava, F., Dec 2023, In: Journal of Medical Ethics. 49, 12, p. 838-843 6 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
4 Citations (Scopus)

100 Downloads (Pure)
Machine learning models in trusted research environments - understanding operational risks
Ritchie, F. (Lead / Corresponding author), Tilbrook, A., Cole, C., Jefferson, E., Krueger, S., Mansouri-Benssassi, E., Rogers, S. & Smith, J., 14 Dec 2023, In: International Journal of Population Data Science. 8, 1, 9 p., 30.
Research output: Contribution to journal › Article › peer-review

Open Access
File
71 Downloads (Pure)
Collection of tools and resources for managing the statistical disclosure control of trained machine learning models
Jefferson, E. & Reel, S., 21 Sept 2022, GitHub.
Research output: Other contribution

Green Paper: Disclosure Control Of Al Algorithms From TREs (GRAIMATTER) lead: University of Dundee other instns: National Services Scotland, University of the West England UWE - Bristol, University of Edinburgh, URV (Spain)

Project Details

Funding

UN Sustainable Development Goals

Fingerprint

Health Informatics Centre

Functional considerations of machine learning and personal data

Trusted Research Environments: defining a common specification and getting them ready for ML workflows

GRAIMatter Public Workshop

Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare

Machine learning models in trusted research environments - understanding operational risks

Collection of tools and resources for managing the statistical disclosure control of trained machine learning models

Green Paper: Disclosure Control Of Al Algorithms From TREs (GRAIMATTER) lead: University of Dundee other instns: National Services Scotland, University of the West England UWE - Bristol, University of Edinburgh, URV (Spain)

Project Details

Funding

UN Sustainable Development Goals

Fingerprint

Equipment

Health Informatics Centre

Activities

Functional considerations of machine learning and personal data

Trusted Research Environments: defining a common specification and getting them ready for ML workflows​

GRAIMatter Public Workshop

Research output

Machine learning models, trusted research environments and UK health data: ensuring a safe and beneficial future for AI development in healthcare

Machine learning models in trusted research environments - understanding operational risks

Collection of tools and resources for managing the statistical disclosure control of trained machine learning models

Trusted Research Environments: defining a common specification and getting them ready for ML workflows