Measure real review conditions
A useful benchmark separates human-only text, AI-only text, mixed-authorship drafts, edited AI output, translated passages, short responses, and domain-specific writing.
Resources
A concise benchmark summary for evaluating AI detector accuracy, false-positive risk, edited drafts, multilingual samples, and review limits.
Open core guideA useful benchmark separates human-only text, AI-only text, mixed-authorship drafts, edited AI output, translated passages, short responses, and domain-specific writing.
Overall accuracy is not enough for high-stakes review. Teams should inspect false-positive rates by language, document length, template use, and writing context before choosing thresholds.
Benchmark summaries should guide triage rules, reviewer training, and evidence requirements. They should not promise perfect authorship proof for an individual document.
It should include sample categories, model families, editing conditions, language coverage, false-positive reporting, confidence bands, and limits on how the results should be used.
No. Benchmark accuracy helps calibrate review workflows, but individual decisions still need passage evidence, document context, policy, and human judgment.