In this episode, hosts Michael Mainelli and Adam Leon Smith welcome Mike Thieme, Managing Director and Cloud Advisory Lead at Accenture. Drawing on his early work in biometric standards since 2005 and extensive experience delivering AI for public and private sector clients, Mike explores the transition to generative AI and the vital need for “consumable” standards that busy professionals can actually use.

He shares insights from leading ISO/IEC projects on AI verification & validation and performance measurement for classification, regression, clustering, and recommendation systems. The discussion covers government AI adoption, reframing human oversight as “human in the lead,” challenges with metrics and reproducibility, and the importance of clear terminology, especially for agentic AI.

Adam’s recent paper AI Agents Under EU Law, which maps compliance architectures for autonomous AI agents, is available at https://arxiv.org/pdf/2604.04604.

A practical, hands-on episode on making AI standards actionable and measurable in the real world.

In this episode, hosts Professor Michael Mainelli and Adam Leon Smith welcome Patrick Sullivan for a practical look at AI certification and ISO/IEC 42001. Patrick explains what third-party certifiers actually do and how they provide objective assurance on AI management systems. He addresses common misconceptions since 42001’s 2023 release, whilst also highlighting the growing market demand, with major firms like Microsoft, Oracle, and Anthropic earning certification and pushing it down supply chains, plus real value in risk management and investment confidence. The discussion covers regional differences in AI governance, the skills needed for effective audits, implementation challenges for organisations of all sizes, and formalising expectations around agentic AI governance. A grounded, certifier’s-eye view on turning the AI standards stack into real-world assurance.

In this episode, hosts Michael Minelli and Adam Smith welcome Nicholas Beale, founder and director at Sciteb, for an insightful look at AI ethics and governance. Nicholas discusses his early internet ethics work and his paper on the Unethical Optimisation Principle, explaining why AI optimisers disproportionately pick unethical strategies by ignoring future downsides. He explores mitigations like panels of AIs, risks of single-system reliance, defence challenges, the Investor Consensus on Responsible AI, guardrail issues, and the need for diversity to avoid systemic risks. A mathematically grounded conversation urging balanced systems that preserve human judgment and the common good.