International evaluation best practice and open questions in AI measurement
The International Network for Advanced AI Measurement, Evaluation and Science (NAAIMES) convened in Seoul on the margins of the 2026 International Conference on Machine Learning to discuss AI evaluation best practices. The Network, established in 2024 and comprising institutes from Australia, Canada, the EU, France, Japan, Kenya, South Korea, Singapore, the UK and the US, released its first best‑practice guidance document and showcased tools such as AISI’s Engineering Playbook. Members highlighted open questions, challenges in evaluating AI agents, and the need for coordinated global standards.
The Core Development
Advanced AI systems are being built and deployed across borders, languages and industries. To develop and deploy AI well, robust ways to evaluate and understand it are needed more than ever. Trust in AI and adoption both hinge on a shared understanding of why AI systems behave as they do. As AI diffuses globally, so too must our approach to evaluation.
That’s why the International Network for Advanced AI Measurement, Evaluation and Science (NAAIMES) – formerly the International Network of AI Safety Institutes – continues to drive forward the science of AI evaluation. Established in 2024, the Network brings together Institutes globally, in Australia, Canada, the European Union, France, Japan, Kenya, the Republic of Korea, Singapore, the United Kingdom and the United States.
This year, on the margins of the 2026 International Conference on Machine Learning, in Seoul, the Network gathered, alongside industry leaders and civil society organisations. Network members discussed the latest in the science of AI evaluation: showcasing evaluation tools (such as AISI’s newly released Engineering Playbook ), raising open questions and challenges encountered in evaluating AI agents, and mapping out current and future best practices.
Deployment & Availability
Network best practices are designed to support the growing ecosystem of third-party evaluators, who play a crucial role in promoting trusted adoption of AI.
Strategic Perspective & Ecosystem Impact
The event in Seoul coincides with the completion of the Network’s first best practice guidance document.
High-quality measurement science is key to governments’ and businesses’ ability to assess AI for a wide spectrum of needs. Agreeing this between institutes with leading expertise in the field should enhance comparability between evaluations and so strengthen the science of evaluations.
This guidance builds on and is intended to complement existing best practice publications – most notably NIST AI 800-2 – while providing specific recommendations to third-party evaluators, such as on capability elicitation. It details:
Why it matters
Robust AI evaluation is essential for building trust and ensuring safe deployment across industries. Standardized best practices enable comparability and support governments and businesses in assessing AI systems.