What "99% Accurate" Really Means in AI Detectors
Accuracy claims hide two very different error types. Understanding them will change how you read every score.
Building bilingual writing-quality tools
Two errors, one number
A detector can fail in two ways: flagging human writing as AI (false positive) or missing AI text entirely (false negative). A single "accuracy" number blends both — and the blend depends entirely on what texts were tested.
Why false positives matter most
For students and professionals, a false positive is the costly error: your own honest writing gets flagged. Non-native phrasing, formal registers, and heavily edited text all raise false-positive risk. This is exactly why scores should inform revision, never verdicts.
Questions to ask any detector
- What languages was it validated on? English-only validation says nothing about Arabic.
- Does it show which sentences drove the score?
- Does it express uncertainty, or pretend to be binary?
Sahihly shows sentence-level highlights and treats every score as an estimate — because that's what it honestly is.
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