The technical–institutional ecosystem for trustworthy AI | IASEAI '26

What does it take to build a trustworthy ecosystem for AI — one that connects technical systems, institutional processes, and regulatory frameworks? This breakout session from IASEAI '26 explores the intersection of AI safety engineering, risk assessment, and governance. Speakers examine how emerging tools like safety cases, certification frameworks, and structured risk methodologies can help bridge the gap between machine learning systems and real-world regulatory requirements. Across two segments, the session highlights both technical and institutional challenges — from certifying AI systems under existing regulatory regimes to improving the objectivity and transparency of AI risk assessment and evaluation practices. Mark Locherer — The missing link to safe AI homologation Matthew Murphy — Making AI Risk Assessment More Objective: A Practical Framework Shaun Feakins — Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases Leon Stauffer — Audit Cards: How to Better Contextualize AI Evaluations Yousra Fettach — Building and Measuring Trust between Large Language Models Maarten Sap — 1-2-3 Check: Enhancing Contextual Privacy in LLM via Multi-Agent Reasoning Hector de Rivoire — Transparency Artifacts and Shared Responsibility Across General-Purpose Technologies: Governance Lessons for AI 📍 Recorded at UNESCO Headquarters, Paris About IASEAI The International Association for Safe and Ethical AI (IASEAI) is a global professional association bringing together researchers, policymakers, and practitioners working to ensure that advanced AI systems operate safely and ethically for the benefit of humanity. Learn more: https://www.iaseai.org #AI #AISafety #AIGovernance #AIpolicy #IASEAI #ArtificialIntelligence #ResponsibleAI