False Positives in AI Detection: What Reviewers Should Check

False positives are one of the most important risks in AI detection. Clear, formal, or heavily edited human writing can sometimes look similar to generated text.

Common causes

Template-based writing, strict academic style, translation, grammar correction, and short samples can all reduce natural variation. Reviewers should mark these factors before escalating.

What to verify

Look for drafts or revision history.

Check whether citations and source details are specific.

Ask whether the text was translated or edited with assistance.

Use sentence-level evidence instead of one score.

GPTZeroAI should support a documented review process, not a shortcut around human judgment.

How to reduce false-positive risk

False-positive review should start with the text itself, but it should not stop there. Compare the result with the false-positive guidance page, check whether the sample is long enough, and ask whether the author used translation, grammar correction, or structured templates.

Evidence to collect before escalation

Earlier drafts or version history.

Source notes, citations, and prompt requirements.

Examples of the same author writing in another setting.

A short reviewer note explaining the final decision.