How AI Detection Works: The Signals Behind the Score

AI detectors feel mysterious, but they’re just statistical classifiers. Understanding the signals they use is the first step to writing with confidence — and to lowering a score when it’s wrong.

The core signal: perplexity

A language model generates text by predicting the most likely next word. Perplexity measures how “surprised” a model would be by each word in your text. Low perplexity means your words were exactly what a model would have picked — a strong hint of machine generation. Human writing regularly surprises, because humans make unconventional choices.

The rhythm signal: burstiness

Burstiness measures variation in sentence length and structure. Humans write in bursts — a short punch, then a long winding clause, then a fragment. AI output is eerily even. A detector treats that uniformity as another fingerprint.

The deeper features

Beyond those two, detectors often look at:

• Syntactic uniformity — repeated sentence shapes and clause patterns.
• N-gram likelihood — the probability of common word sequences.
• Stylistic flatness — lack of idiom, personality and concrete detail.

How the score comes together

A classifier weighs these features and outputs a probability: “this text is X% likely AI-generated.” Different tools weight the signals differently, which is why the same paragraph can score 30% on one detector and 80% on another. You can measure this yourself with an AI detector.

Why this is good news

Because detection measures patterns, not intent, the score is mutable. Vary your rhythm, drop formulaic transitions, and add concrete specifics, and the score falls. A tool can automate that: DeAIze humanizes flagged sentences in a detection-feedback loop while preserving your meaning. See it on the homepage, or review pricing for word packs.