When a detector flags writing as machine-made, is it reacting to word choice, style, and structure, or to something else?

This explores what AI text detectors actually pick up on when they label writing as machine-made, and whether those signals are the same ones people think they're noticing. The corpus points to three layers of signal: how varied the vocabulary is, habits of style and argument, and the structure of a whole story. A fourth finding is less comfortable. Some detectors respond to 'AI-ness' when you meant them to measure something else.

The most basic layer is vocabulary. When researchers break word choice into six measurable dimensions (how many different words appear, how evenly they're spread, how far apart in meaning they are, and so on), ChatGPT's writing differs from human writing in statistically clear ways Can human judges detect measurable differences in AI text?. Newer models drift further from human patterns on these measures, yet they get harder for people to spot Can humans detect AI text if machines can measure it?. Even trained linguists miss the difference. Across 30 studies covering text, images and voice, human accuracy sits near chance Can people reliably spot content made by AI?. So the signal is real, but it lives in statistics that humans can't see.

The second layer is style and rhetorical habit. On Reddit's r/ChangeMyView, simple and readable linguistic features, combined with measures of argument quality, flagged LLM-written counter-arguments with 99% accuracy. That matches heavy neural detectors Can simple linguistic features detect AI-written arguments?. Two giveaways stood out. LLMs echo and adapt to the prompt they were given, and their arguments look too much like a textbook. The third layer goes deeper than style. In fiction, a detector that ignored style completely and looked only at narrative choices still reached 93% accuracy. Those choices include how much agency characters have and whether events are told in order Can AI stories be detected without analyzing writing style?. This matters for evasion: swapping words can hide style, but structural choices only change if you rewrite the story. Whether heavy rewriting actually fools detectors is still untested. One paper claims it does but ran no detector experiments Do rewrites that hide authorship also fool AI detectors?.

The surprise is what happens when detectors meet AI style without being built for it. Fake-news classifiers flag truthful LLM-written articles as fake and pass human-written disinformation as genuine Why do fake news detectors flag AI-generated truthful content?. They learned to recognize a writing style and treated it as a sign of lying. They never actually checked whether the content was true. Human 'slop' accusations show a similar mismatch. In 25 million Hacker News and Reddit comments, the prose features that really separate AI from human text did not predict which comments got called AI slop Do AI slop accusations actually detect AI text?. The label works as social policing, not detection. Even platform-level measurements rest on these tools: a 9% 'AI share' in some Reddit communities is one detector's flagging rate, not a direct count How much machine-generated text actually appears on Reddit?.

A gap in this collection: none of these notes covers the probability-based methods many commercial detectors use. Those methods ask how predictable each word is to a language model. Watermarking is also missing. What the corpus does show is that 'AI-ness' is a measurable property that people can't feel. Any classifier trained near it, even one built for a different job, can end up detecting that property instead.