AI writing detector scores are being treated like evidence. They aren’t.

I keep seeing schools, editors, and clients act as if an “84% AI-generated” score proves who wrote something.

But what does that percentage actually mean?

Unless the detector publishes its false-positive rate, explains how it performs on short samples, and has been tested on writing by non-native, the number is mostly a warning label rather than proof of authorship.

The standard should be pretty simple:

• Don’t penalize someone based on one detector.
• Test the tool on known human, AI, and heavily edited samples.
• Avoid making decisions from short passages.
• Require a human review of the writing and surrounding context.
• Give the writer a chance to provide drafts, notes, version history, or sources.
• Publish an appeals process before using detection at all.

What I mean is that it’s better to treat these tools as a way to spot patterns that may trigger AI detection. Checkers like Getsolved also include additional tools like grammar and fact-checking, which make them more useful as part of a broader review.

Treating the score as the only proof can put people in the position of having to prove they wrote their own work. And we’re already seeing cases like this.

Has anyone run the same samples through multiple detectors? If so, share the sample length, tools, and scores, not just which detector “won.” I’d especially like to see results for edited AI text, older human writing, and work by non-native English writers.