AI safety standards worldwide must keep up with the rapid development and deployment of AI technology. Our mission is to help accelerate the writing of AI safety standards.
Recent outputs & updates
-
Principles and Guidelines for Randomized Controlled Trials in AI Evaluation
We have published a paper setting out five principles and 33 guidelines for randomised controlled trials measuring how AI access affects human behaviour, performance, or wellbeing, filling a gap in shared methodological standards.
-
The Case for ESM3 as a General-Purpose AI Model with Systemic Risk Under the EU AI Act
We examine whether biological foundation models like ESM3 meet the EU AI Act’s criteria for systemic risk. Despite exceeding relevant compute thresholds, ESM3 falls outside current classification guidance because it does not generate human language. We propose four remedies to close this gap.
-
Scorecard of AI Benchmark Quality
We introduce the “Scorecard of AI Benchmark Quality” to grade benchmarks across a set of dimensions reflecting its quality. We then use those scores to map the benchmark to appropriate use-cases. We presented our paper at the Evaluating Evaluations (EvalEval) Workshop of ACL 2026 in San Diego.
-
Open Problems in AI Incident Governance
We examine AI incident governance across regulatory and independent frameworks, identify open problems in how incidents are defined, classified, monitored, and reported, and propose monitoring and reporting principles, guidelines, and a reporting template.