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| Formaat: | Recurso digital |
| Taal: | Engels |
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Zenodo
2026
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| Online toegang: | https://doi.org/10.5281/zenodo.19255946 |
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| _version_ | 1866901797883346944 |
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| author | Clarke, Laura Nemade, Kshitij Adamson, Jack Joannides, Alexis |
| author_facet | Clarke, Laura Nemade, Kshitij Adamson, Jack Joannides, Alexis |
| contents | <div> <p><span lang="EN-GB">This report outlines how the Eastern England Secure Data Environment (EE</span><span lang="EN-GB">‑</span><span lang="EN-GB">SDE) has been preparing to support safe and trustworthy use of artificial intelligence (AI) and machine</span><span lang="EN-GB">‑</span><span lang="EN-GB">learning (ML) models trained on sensitive health data. As part of VISTA, one of DARE UK’s Early Adopter projects, the EE</span><span lang="EN-GB">‑</span><span lang="EN-GB">SDE deployed and evaluated new tools designed to help Trusted Research Environments (TREs) assess and manage privacy risks linked to AI projects. </span> <br><br></p> </div> <div> <p><span lang="EN-GB">A key outcome of this work is the VISTA AI Risk Assessment Toolkit, which provides a structured way for TREs to review proposed AI projects, understand their data needs, and ensure that appropriate safeguards are in place from the outset. The toolkit works alongside SACRO</span><span lang="EN-GB">‑</span><span lang="EN-GB">ML, a new disclosure</span><span lang="EN-GB">‑</span><span lang="EN-GB">control technology that checks trained ML models for signs that they might reveal information about individuals in the training data. Together, these tools help reviewers make clearer, evidence</span><span lang="EN-GB">‑</span><span lang="EN-GB">based decisions about whether a model can safely be released. </span> <br><br></p> </div> <div> <p><span lang="EN-GB">The project shows that AI safety checks can be integrated into real research workflows without disrupting analysis. It also highlights areas for future improvement, including clearer guidance, better onboarding materials, and continued collaboration across the TRE community to build consistent and trusted approaches to responsible AI research.</span> </p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19255946 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Eastern England SDE Safe Models, Safe AI: Governance and Disclosure Testing Clarke, Laura Nemade, Kshitij Adamson, Jack Joannides, Alexis VISTA TREvolution SACRO-ML Model Disclosure Control <div> <p><span lang="EN-GB">This report outlines how the Eastern England Secure Data Environment (EE</span><span lang="EN-GB">‑</span><span lang="EN-GB">SDE) has been preparing to support safe and trustworthy use of artificial intelligence (AI) and machine</span><span lang="EN-GB">‑</span><span lang="EN-GB">learning (ML) models trained on sensitive health data. As part of VISTA, one of DARE UK’s Early Adopter projects, the EE</span><span lang="EN-GB">‑</span><span lang="EN-GB">SDE deployed and evaluated new tools designed to help Trusted Research Environments (TREs) assess and manage privacy risks linked to AI projects. </span> <br><br></p> </div> <div> <p><span lang="EN-GB">A key outcome of this work is the VISTA AI Risk Assessment Toolkit, which provides a structured way for TREs to review proposed AI projects, understand their data needs, and ensure that appropriate safeguards are in place from the outset. The toolkit works alongside SACRO</span><span lang="EN-GB">‑</span><span lang="EN-GB">ML, a new disclosure</span><span lang="EN-GB">‑</span><span lang="EN-GB">control technology that checks trained ML models for signs that they might reveal information about individuals in the training data. Together, these tools help reviewers make clearer, evidence</span><span lang="EN-GB">‑</span><span lang="EN-GB">based decisions about whether a model can safely be released. </span> <br><br></p> </div> <div> <p><span lang="EN-GB">The project shows that AI safety checks can be integrated into real research workflows without disrupting analysis. It also highlights areas for future improvement, including clearer guidance, better onboarding materials, and continued collaboration across the TRE community to build consistent and trusted approaches to responsible AI research.</span> </p> </div> |
| title | Eastern England SDE Safe Models, Safe AI: Governance and Disclosure Testing |
| topic | VISTA TREvolution SACRO-ML Model Disclosure Control |
| url | https://doi.org/10.5281/zenodo.19255946 |