AI Detector Accuracy Explained

What accuracy means in AI detection

AI detector accuracy is not just a single percentage. It depends on text length, editing history, language, topic, and whether the writing was fully generated, lightly assisted, or human-written with formulaic structure.

Start with the overview at AI detector accuracy, then compare edge cases with AI detector false positives. For a deeper view of scoring logic, use the methodology page.

False positives and false negatives

A false positive marks human writing as AI-like. A false negative misses AI-assisted text. Both matter. Short texts, polished templates, and non-native writing can be harder to classify. Long drafts with consistent paragraph patterns usually provide more evidence.

Better interpretation workflow

Use confidence bands, passage-level highlights, and reviewer notes. Ask what decision will be made from the result and what additional evidence is needed. In academic, hiring, publishing, or compliance settings, a detector should trigger review, not act as the final decision maker.

FAQ

Why do accuracy claims vary by vendor?

Vendors test on different datasets, languages, text lengths, and definitions of AI assistance. A useful accuracy page should explain the testing context instead of presenting a number without boundaries.

How should low-confidence results be handled?

Low-confidence results should be routed to human review or treated as inconclusive. They are useful for prioritization, not final decisions.

What improves reliability?

Longer samples, clear document type, passage-level review, and comparison with known writing all improve interpretation. Strong processes reduce the harm caused by both false positives and false negatives.