Many people have called for improving benchmarks, but which ones are actually good and which ones truly need further development? AI Standards Lab has set out to clarify the benchmark landscape.
We introduce the “Scorecard of AI Benchmark Quality” to grade benchmarks across a set of dimensions reflecting their quality. We then use those scores to map the benchmark to appropriate use-cases. We expect that the scorecard can be used by regulators and model developers to define minimum quality standards for benchmarks involved in high-stakes decisions, developers to iteratively improve their benchmark, and for the general public to trust models based on benchmarks used to assess them.
Paper Authors: Ayrton San Joaquin, Ze Shen Chin, Rokas Gipiškis
We presented our paper at the Evaluating Evaluations (EvalEval) Workshop of ACL 2026 in San Diego, and we are currently working on improving the paper further to create a more concrete scorecard.
This paper builds on our earlier working draft, “A Scorecard for the Quality of AI Evaluations” (February 2026).