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When an AI detector incorrectly classifies human-written text as AI-generated, potentially causing unfair consequences for the author.
False positives in AI detection occur when a detector flags human-written text as machine-generated. This can happen with formulaic writing (legal documents, technical manuals), non-native English speakers' text, or highly structured content. False positive rates vary by tool — ranging from 1% to 15% — and are a major concern in academic integrity contexts. To mitigate false positives, best practice is to use multiple detection tools and never rely on a single score for consequential decisions.