Refresh benchmarks when models change
Detector methodology should be revisited when new model families, editing tools, or writing workflows become common. Static claims age quickly in AI detection.
Resources
How AI detection methodology should be updated as models, writing tools, multilingual usage, and review policies change.
Open core guideDetector methodology should be revisited when new model families, editing tools, or writing workflows become common. Static claims age quickly in AI detection.
Methodology updates should document where human writing is most often misread: short samples, translated work, templates, polished edits, and citation-heavy documents.
Every methodology update should explain how scores, confidence bands, passage evidence, reviewer notes, and appeal paths should be interpreted in real workflows.
It should be reviewed whenever major model behavior, editing tools, benchmark data, language coverage, or institutional policy changes affect how detector evidence is interpreted.
A useful update explains what changed, which samples were reviewed, how false positives were checked, what limitations remain, and how reviewers should apply the new guidance.