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International Network for Advanced AI Measurement, Evaluation and Science

What International Network for Advanced AI Measurement, Evaluation and Science does

International Network for Advanced AI Measurement, Evaluation and Science (NAAIMES) is an international coordination consortium focused on strengthening the science and shared best practices that underpin the measurement and evaluation of advanced AI capabilities. In its current framing, the Network was established in November 2024 and brings together government bodies (including an EU member) from Australia, Canada, the European Union, France, Japan, Kenya, the Republic of Korea, Singapore, the United Kingdom, and the United States to develop internationally recognised approaches for evaluating advanced AI capabilities. The Network’s work emphasizes (1) evaluation approaches that are objective-driven, (2) transparency and reproducibility to support confidence and comparability across evaluations, and (3) open research questions—such as how to use risk models, how evaluators should prioritize with limited resources, what information should be shared vs. withheld, and how to evaluate AI systems rather than only model artifacts. The Network has also been described in public materials as a “refocusing” of the earlier “International Network of AI Safety Institutes” toward measurement and evaluation science, with the UK taking a coordinating role in 2026.

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Company record

Source map · 2 recurring channels · 42 references
International Network for Advanced AI Measurement, Evaluation and ScienceOfficial Site · monitor↗MTS Frontier / Control RoomCurated Feed · monitor↗alignmentproject.aisi.gov.ukOfficial Reference · reference↗Official organization sourceOfficial Reference · reference↗aisi.go.jpReporting · reference↗aiwiki.aiReporting · reference↗algorithmwatch.orgReporting · reference↗arxiv.orgReporting · reference↗assets.publishing.service.gov.ukReporting · reference↗casrai.orgReporting · reference↗centeraipolicy.orgReporting · reference↗committees.parliament.ukReporting · reference↗commonsbusiness.parliament.ukReporting · reference↗csis-website-prod.s3.amazonaws.comReporting · reference↗csis.orgReporting · reference↗digital-strategy.ec.europa.euReporting · reference↗en.wikipedia.orgReporting · reference↗evals.alignment.orgReporting · reference↗futureoflife.orgReporting · reference↗g7g20-documents.orgReporting · reference↗gov.ukReporting · reference↗governingtransformativeai.substack.comReporting · reference↗hansard.parliament.ukReporting · reference↗industry.gov.auReporting · reference↗ised-isde.canada.caReporting · reference↗itpro.comReporting · reference↗lexisnexis.comReporting · reference↗ncme.orgReporting · reference↗news.pm-global.co.ukReporting · reference↗nist.govReporting · reference↗papers.ssrn.comReporting · reference↗proxy.goincop1.workers.devReporting · reference↗qna.files.parliament.ukReporting · reference↗reddit.comReporting · reference↗sgaisi.sgReporting · reference↗siia.netReporting · reference↗sotaletters.substack.comReporting · reference↗static1.squarespace.comReporting · reference↗techpolicyinstitute.orgReporting · reference↗telecompaper.comReporting · reference↗thefuturesociety.orgReporting · reference↗thwingetal.appReporting · reference↗time.comReporting · reference↗wp.oecd.aiReporting · reference↗

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Funding

Latest disclosed valuationNot disclosed
Tracked capital$32M3 sourced rounds

What it builds

Consensus areas on practices for automated AI evaluations (published list of practices and open questions)

The Network published a list of key practices and open questions intended to guide measuring and evaluating AI capabilities, including consensus principles (e.g., clear evaluation objectives; transparency and reproducibility) and unresolved issues (e.g., prioritization, information sharing, and evaluating AI systems rather than just models).

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Best-practice guidance for third-party evaluators

In 2026, the Network described completion of its first best-practice guidance document, intended to support an ecosystem of third-party evaluators and designed to improve comparability and evaluation workflow choices (including how objectives and benchmark selections are defined and how elicitation is iterated).

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Milestones & partnerships