Spotting AI writing is about a coin flip for people, and the research doesn't say how often they wrongly blame a human.

This explores the false-alarm side of AI detection: how often people look at something a human wrote and decide a machine wrote it. The corpus has no single percentage for that specific error. What it does show is that the error is built into how people judge text. A 30-study systematic review found that human accuracy at telling AI content from human content generally clusters around chance, across text, images and voice, and hasn't improved as AI output has become more realistic Can people reliably spot content made by AI?. Chance-level accuracy means that, overall, people are wrong about as often as they're right. The review summary doesn't split those errors into the two directions, so we can't say how many are human writing mistaken for AI versus AI text accepted as human.

The surprising part is that the differences are real, just not visible to people. Statistical analysis finds that LLM text differs from human writing on six measures of vocabulary richness and spread. Yet human judges, including trained linguists and NLP researchers, can't reliably detect those differences Can human judges detect measurable differences in AI text?. Newer models drift further from human patterns while becoming harder for people to spot Can humans detect AI text if machines can measure it?. So when a reader says "this sounds like AI," they're usually reacting to something other than the signals that actually separate the two. That gap is where false accusations come from.

The most direct evidence on mistaking human writing for AI comes from studying accusations in the wild. When researchers looked at online comments that other users had accused of being AI-generated, the comments lacked the features that actually distinguish AI text from human writing Do unfounded AI accusations harm human writers instead?. The authors argue these accusations work more as gatekeeping than as detection, and that the harm falls on human writers whose credibility gets dismissed. That turns the usual worry around: the problem isn't only AI passing as human, but humans being treated as AI.

Two threads point to where better judgments might come from. Writing style is a weak tell, but narrative structure is a strong one. A system looking only at choices like how much agency characters have and how the timeline is ordered separated AI fiction from human fiction with 93% accuracy, even with stylistic cues removed Can AI stories be detected without analyzing writing style?. Human readers tend to focus on surface style, which is exactly the layer that misleads them. Second, believing who wrote something changes how it gets judged: in one experiment, AI judges went easier on a rule-breaking piece of writing when told a human wrote it, while human judges got stricter Do authorship labels change how AI judges evaluate rule violations?. Who people think the author is shapes their verdict, not just what's on the page.

The honest answer: the corpus establishes that people misjudge authorship constantly and that real human writers get wrongly accused. It doesn't give a clean rate for human-to-AI misclassification specifically. If you want that number, look for detection studies that report false-positive rates separately from overall accuracy. The material here suggests that number would be high and would say more about readers' suspicions than about the writing itself.