False positives
An AI detector false positive happens when human-written text is flagged as likely AI-generated. This risk is the main reason detector output should be treated as a review signal, not as final proof.
Detectors look for statistical and stylistic patterns. Human writing can share those patterns when it is formal, generic, short, heavily edited, template-based, or written by someone using a second language.
Do not treat the score as a verdict. Review the highlighted text, ask for process evidence, compare with prior writing, and give the author a chance to explain or revise.
ClearText keeps uncertainty visible and turns risky signals into revision suggestions. That makes it better suited to supportive review than punishment.
Situation
Safer response
Risky response
High detector score
Request context and revision
Assume misconduct
Short sample
Ask for more writing or process evidence
Overinterpret limited text
Understand and challenge questionable detector results.
Build a fair review process.
Ask for targeted revisions without accusing writers.
It is a human-written text that a detector incorrectly flags as AI-generated.
Rates vary by detector and sample type, but they are possible in every AI detection workflow.
Use longer samples, review context, and avoid relying on one score.
No. It can help explain writing signals and support a more careful review.