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This study analyzed 135,389 document pairs from a professional English editing service to investigate how editing influences AI text detector responses while controlling for authorship and content. The results revealed that false positive rates (FPRs) for human-written texts varied significantly across 13 AI detectors, with some showing FPRs as high as 100% depending on the extent of editing. These findings highlight professional editing style as a critical confounding factor in AI detection, questioning the reliability of these tools in academic contexts.
Professional editing can dramatically skew AI text detector outputs, with false positive rates for human-written texts reaching as high as 100% depending on the detector used.
AI text detectors are increasingly employed in academic settings, but it remains unclear whether their outputs reflect AI authorship itself or broader linguistic features associated with polished academic English. Previous studies have reported high false-positive rates (FPRs) for non-native English writing, but population-level comparisons confound authorship with differences in topic, domain, and writing style. Professional editing provides a useful setting for examining this issue because it changes the linguistic form of manuscripts while preserving authorship and content. We examined 135,389 document pairs from a professional English editing service (2018-2025), comprising non-native manuscripts and their native-edited versions, to assess how editing affects detector responses controlling for content and authorship. For the 13 AI text detectors, FPRs for human-written texts varied widely, from 0.0% to 100.0%. Responses varied across detectors: the same edits increased AI scores in some detectors but decreased them in others. Notably, score changes correlated with the extent of editing. The findings identify professional editing style as a key confounding variable in AI detector outputs, rather than establishing a full separation of text origin from linguistic style, raising concerns about fairness and reliability in academic settings.
