The Value of AI Verification Hinges on Human Equivalency
cjmaddison · x · 2026-08-03
The author explores how to measure the value of a "verification": the core criterion is to ask how meaningful the result would be if a human passed the exact same verification test.
- Biological Data Mining: If a human validated an insight merely by mining a biological database, it wouldn't be considered an established biological fact.
- Lean Proofs: If a human provides a Lean-checked proof, it's more or less a mathematical fact (modulo potential Lean bugs).
This implies that when evaluating AI verification results, we should consider whether the verification process itself holds substantial value for humans.
Related event: Evaluating AI Verification: Data Mining vs Formal Proofs(3 posts)→
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