Panelists & Speakers

The EU AI Act establishes the first regulatory framework for AI. As AI rapidly evolves, the European Commission's AI Office develops and implements policies that ensure safety, accountability, and innovation.

This keynote outlines the EU’s approach to regulating General Purpose AI under the AI Act. It highlights key initiatives led by the AI Office and how to engage with them. It includes the Codes of Practice for regulatory guidance, a Tender on AI Safety to build evaluation and enforcement capacity, and a Network of Evaluators to advance scientific evaluations. Additionally, it presents the Scientific Panel, which aims to provide technical and scientific input for the enforcement of the AI Act. 

Automated LLM-based agent evaluations have become a standard for assessing AI capabilities in both industry and government, but current reporting practices focus on what agents accomplish without resolution on how they accomplish it. In this talk I will discuss how UK AISI mines evaluation transcripts to (i) detect issues in evaluation tasks that could lead to mis-estimating capabilities, and (ii) understand how agent capabilities are evolving. I will survey a selection of AISI's methods, tools, and results, and outline research opportunities for better analysis instruments and their connection to safety and governance.

The widespread deployment of general-purpose AI (GPAI) systems introduces significant new risks. Based on a collaboration between experts from the fields of software security, machine learning, law, social science, and policy, we design and propose new flaw reporting and coordination measures for GPAI systems, including flaw report forms designed for rapid triaging, AI bug bounty programs, and coordination centers for universally transferable flaws, that may pertain to many developers at once. By promoting robust reporting and coordination in the AI ecosystem, these proposals could significantly improve the safety, security, and accountability of GPAI systems.

Audits are increasingly used to identify risks in deployed AI systems, but current audit tooling often falls short by focusing narrowly on evaluation while neglecting key needs like harms discovery, audit communication, and support for advocacy. Based on interviews with 35 practitioners and a landscape analysis of over 400 tools, I outline how this limited scope hinders effective accountability.  Yet even where tools do focus on evaluation, they often rely on monolingual and decontextualized methods that fail to capture real-world model behaviour. I illustrate this through a case study on multilingual evaluation, where we developed functional benchmarks in six languages. These benchmarks reveal significant cross-linguistic fragility in LLM performance and underscore the risks of governance frameworks that assume language-agnostic capability. Together, these findings point to the need for a more expansive vision of technical governance that centers contextual robustness, and the infrastructural conditions for meaningful accountability.