Do AI Detectors Work?
Short answer: they can detect some machine-like signals, but they are not reliable enough to serve as standalone proof.
How detectors usually work
- Statistical rhythm signals: low variance and low burstiness can increase suspicion.
- Lexical patterns: repeated phrase templates and high-frequency assistant vocabulary.
- Classifier models: probability estimation trained on mixed human/AI datasets.
Why false positives happen
Formal writing, non-native writing, academic prose, and even historical texts can share the same statistical surface features as generated drafts. A high detector score can indicate overlap in style patterns, not definitive AI authorship.
Operational policy recommendation
- Use detector scores as triage signals, not verdicts.
- Require pattern-level evidence and editable diagnostics.
- Preserve provenance artifacts (draft history, edits, timestamps).
- Document known false-positive cases for internal calibration.
Method links
For transparent interpretation, read How It Works, Limitations, and Pattern Library.
Try a pattern-first approach
Run the WROITER Diagnostic and inspect concrete flags (patternId, severity, detectorNote) before making publishing or review decisions.