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How AI Content Detectors Work and Why They Often Misfire

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AI content detectors do not actually know whether text was machine-generated; they estimate likelihood from statistical patterns. Text-based tools commonly measure perplexity, which captures how predictable the wording is, and burstiness, which captures how much that predictability varies across sentences. Modern systems also train classifiers on large corpora of human and AI writing to pick up subtler stylistic signals. Image detectors instead look for pixel and frequency artifacts and provenance metadata such as C2PA. Accuracy drops for short texts, heavily edited AI output, and writing by non-native English speakers, so experts advise treating scores as one input for human review rather than proof.

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