One figure that consistently appears in evaluations is 98% claimed detection accuracy in benchmark tests conducted with controlled AI writing datasets. That number tends to appear in demonstrations where the model is given clearly artificial text that contains few human editing patterns. In those controlled conditions the system can rely heavily on statistical signals such as low perplexity and repetitive phrasing.

The reason the number looks so high lies in how benchmark environments are constructed. Evaluations often compare fully generated AI essays against entirely human written material, which creates an unusually clear statistical separation. That structure allows detectors to perform closer to theoretical limits because mixed authorship cases are minimized.

Researchers frequently cite 3–7% false positive rate when evaluating how often human writing is mistakenly flagged as AI generated. That range may appear small at first glance, yet it becomes significant when applied to thousands of student essays or newsroom submissions. Even a modest misclassification percentage can create real consequences for authors.

The source of these errors usually comes from stylistic overlap between structured human writing and language model patterns. Writers who use consistent sentence rhythm or formulaic academic phrasing sometimes trigger signals that resemble machine generated text. Detection systems rely on probabilities rather than intent, which means statistical similarities can produce misleading results.

Detection reports frequently reference 70% AI probability threshold as the level where text begins to trigger automated alerts. Scores below that point tend to appear in a neutral category that requires additional interpretation. Thresholds therefore function as statistical guidance rather than absolute proof.