When your own text comes out as AI: what false positives are
A false positive appears when a person writes in a way the detector associates with models — short, even sentences, technical language, cautious phrasing. It happens most often to non-native speakers, technical writers and people who write to a template.
Who runs into it most often
Authors who write in a structured, terse way. Students who stick to a methodology. People writing in a language that is not their first.
What they have in common is that their text has little variance — and variance is one of the things detectors watch.
How to recognise it
Look at which sentences the tool marked. If they are definitions, methodology passages and quotations of standards, it is most likely a false alarm.
If the marked passages are exactly the ones where the author should be writing most in their own voice — the introduction, the conclusion, their own reflection — that is a different signal.
What to do about it
Don't use the result as an accusation. Ask about the content: why this approach, where this figure comes from, what they would change.
For your own texts, the history helps. Once it holds a few of your older pieces, DetekceGPT compares the new text against them and a false alarm becomes easier to spot.
FAQ
Can I disprove a false positive somehow?
Best of all with document versions, notes and the ability to talk about the content. A detector on its own disproves and proves nothing.
Does rewriting the text in other words help?
It usually lowers the score, but it does not solve the problem. If you wrote the text yourself, there is no reason to redo it for the sake of a number.
Does the opposite happen too?
Yes. A generated text that was properly rewritten passes. That is why the result cannot be taken as proof in either direction.
Try it on your own text
Paste a text or upload a file and look at the score and at the specific sentences that came out suspicious.
Run a detection