AI detectors work by measuring statistical properties of text — not by reading for meaning. They analyze how predictable word choices are, how uniform sentence lengths are, and how repetitive the vocabulary is. Collectively, these signals distinguish AI-generated text from human writing with reasonable — but not perfect — accuracy.
Understanding how detectors work is the first step to revising writing more thoughtfully.
The Core Signals#
Most AI detectors measure some combination of three signals:
1. Perplexity#
Perplexity is a measure of how surprising a text is, word by word.
Language models are trained to predict the next word. When you run AI-generated text through a statistical model, each word choice is highly probable — the model picked it because it was the most likely continuation. This makes AI text low-perplexity.
Human writers make unexpected choices. We use unusual words, write run-on sentences, start clauses we do not finish. This makes human text high-perplexity.
Detector logic: Low perplexity → likely AI. High perplexity → likely human.
2. Burstiness#
Burstiness measures sentence length variation.
Humans write with rhythm. Short sentences. Then a much longer one that develops a thought, adds qualifications, and winds toward a conclusion. Then a fragment. AI models write sentences of similar length, one after another, because they optimize for coherent paragraph structure.
Detector logic: Low burstiness (uniform lengths) → likely AI. High burstiness (varied lengths) → likely human.
3. Entropy#
Entropy measures vocabulary diversity.
AI models have preferred vocabularies. Certain phrases appear at abnormally high frequencies in AI text: "delve," "leverage," "crucial," "it is important to note," "in today's world," "paramount," "utilize." These words are not wrong — they are just overused by AI systems trained on similar corpora.
Detector logic: Low entropy (repetitive vocabulary) → likely AI.
How Detectors Combine These Signals#
Detectors do not use a single threshold. They train classifier models on large datasets of known-human and known-AI text, then use those models to assign probability scores.
Some detectors show per-sentence highlighting — identifying which specific sentences are most likely AI-generated. Others give a single document-level score.
This matters because editing just the high-probability sentences can move the overall score significantly.
Why Detectors Are Not Perfect#
Detectors produce false positives and false negatives because:
- Domain specificity: Highly technical or formal writing naturally has low perplexity (jargon is predictable). Academic papers by humans often score as AI.
- Model updates: Newer AI models write with more variation, which reduces detection accuracy.
- Editing: Lightly edited AI text can score as human; heavily edited human text can score as AI.
- Language: Most detectors are optimized for English; accuracy drops significantly for other languages.
What This Means for Your Writing#
If you use AI tools in your writing process, the practical implication is:
- Vary your sentence length deliberately. One short sentence per paragraph changes the burstiness score significantly.
- Replace high-frequency AI phrases. "Utilize" → "use." "Delve into" → "explore." Small changes, large signal impact.
- Add specific, concrete detail. AI tends toward generality; concrete specifics raise perplexity.
- Run your text through a detector while revising. Use Naturalistic AI's detector to review your score and see which sections need another pass.
Check Your Own Writing#
Naturalistic AI's AI Detector runs your text against the same statistical signals detectors use — perplexity, burstiness, entropy — and shows you which passages are most likely to be flagged. If your score is high, the AI Humanizer can help you rewrite sections for better flow and readability.