How AI detectors work and why a score alone can’t prove authorship

AI writing detectors analyze completed text for statistical patterns associated with language models rather than observing how a document was created, so their scores should be treated as signals for further review rather than proof of authorship. Different tools define percentages differently, such as an estimated probability of AI generation or the proportion of prose flagged, and their results depend on training data, scoring thresholds, document length, format, language, editing, and familiarity with the AI models and writing styles involved. Measures such as perplexity and burstiness describe predictability and variation in language but cannot uniquely identify AI writing, while lower detection thresholds increase both detections and false positives. Because AI-generated text may be uncommon in a collection, even a low false-positive rate can make a substantial share of flagged documents human-written, and research has found uneven performance on unfamiliar models, altered text, and writing by non-native English speakers. Watermarks can provide additional evidence when supported by a generating system and preserved through editing, but they cannot establish that unmarked text is human-written. Decisions with meaningful consequences should therefore combine detector results with evidence such as draft histories, notes, disclosed AI interactions, source records, author explanations, and the applicable policy.

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