Red teaming is rapidly becoming a critical component of AI oversight. In this episode, Rumman Chowdhury explains how structured adversarial testing can uncover system vulnerabilities, model failures, and misuse pathways.

The discussion focuses on practical red-teaming approaches that supervisory authorities can adopt, even with limited resources.

Speaker: Rumman Chowdhury (Human Intelligence)
Interviewer: Mirela Kmetic-Marceau, Project Consultant, Ethics of AI Unit, UNESCO

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A deep dive into the metrics and methodologies essential for robust AI evaluations. Agnès Delaborde examines measurement challenges, standards alignment, and the tools supervisory authorities need to assess AI system performance.

The conversation highlights gaps between emerging benchmarks and real-world regulatory needs.

Speaker: Agnès Delaborde (Laboratoire national de métrologie et d'essais – LNE)
Interviewer: Lihui Xu, Programme Specialist, Ethics of AI Unit, UNESCO

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This episode explores how supervisory authorities can translate high-level AI risk principles into practical, operational risk-mapping processes. Nathalie Cohen discusses evaluation frameworks, data considerations, and real-world challenges identified during the roundtable exercise, providing regulators with concrete steps for structuring risk identification and prioritisation.

Speaker: Nathalie Cohen (OECD)
Interviewer: Max Kendrick, AI Strategy Coordinator & Senior Advisor, Office of the Director General, UNESCO

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