So You Want a Trustworthy AI Evaluation? Here's What Actually Matters
OpenAI argues that as frontier AI models have become more capable agentic systems, traditional evaluation methods are no longer sufficient, and third-party evaluations must now carefully account for the "harness" — the tools, scaffolding, and setup surrounding a model — since harness choices can significantly change measured performance. Evaluations should clearly specify what type of claim they are testing (capability elicitation, safeguard performance, or comparison) and provide evidence addressing validity risks such as reward hacking, sandbagging, contamination, refusals, and broken problems. The article recommends that evaluation reports include detailed documentation of harness choices, budgets, elicitation methods, and validity checks, and calls for these practices to be incorporated into emerging national and international AI evaluation standards.