A 7-question checklist for judging AI benchmarks: contamination, metrics, and independence
goyalshaliniuk · x · 2026-10-08
The author lays out a benchmark checklist to run through before comparing two AI systems: What is being measured → Data sources → Contamination (did the data leak into training) → Metric validity → Reality (does the score reflect the real world) → Reproducibility → Independence (who created and ran the benchmark, are results independently verified, are failures reported alongside successes). The core point: a benchmark isn't valuable because it produces a big number — it's valuable when you understand what that number actually represents.
Related event: Seven Questions to Ask Before Trusting an AI Benchmark(3 posts)→
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