False positive
When a detector flags human-written text as AI-generated.
A false positive in AI detection is when the detector incorrectly classifies human-written text as AI-generated. It's the more costly error in most product settings — accusing a student or applicant of AI use when they wrote the work themselves causes real harm.
Production detectors typically tune for a low false-positive rate at the cost of more false negatives. That's why AI Detector API returns a continuous score and a confidence band, not a binary label: it gives your product the choice of how strict to be.
To reduce false-positive risk in your integration, set a higher action threshold (e.g., 0.9+), require sentence-level evidence, and never auto-action on detector output alone. Treat the score as one signal among several.
Related terms
- False negative— When a detector misses AI-generated text and classifies it as human.
- Confidence score— A 0-to-1 value reflecting how certain the detector is that a piece of text was AI-generated.
- AI detection— The task of identifying text that was written by a large language model rather than a human.
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