How AI Detectors Actually Work — and Why They Get It Wrong

AI detectors are not magic. They are statistical models trained to distinguish text that follows the smooth, predictable probability distributions of large language models from text that has the rougher, more variable cadence of human writing.

The two numbers that matter

Every major detector reduces your text to two measurements.

Perplexity measures how "surprised" a language model is by each word in your text. LLMs choose words based on probability — text generated this way has very low perplexity. Human writers are less predictable. We reach for unusual metaphors, use domain-specific terms, make choices that reflect our voice rather than statistical frequency.

Burstiness measures the variance in your sentence structures. Human writing has rhythm — a long complex sentence is often followed by a short one. LLMs produce more uniform text, optimizing for coherence which produces smooth but monotonous prose.

Low perplexity + low burstiness = high AI probability.

Why false positives happen

These statistical signatures appear in human writing too:

• ESL writers produce text with low burstiness and limited vocabulary range
• Technical writing is inherently low-perplexity — domain terminology limits word choices
• Heavily edited drafts have the smooth flow that detectors flag as AI
• Academic writing style emphasizes precision over variety

This is why a carefully polished human essay can score 80%+ AI on multiple detectors.

What this means for you

Understanding the underlying model means you can write in ways that naturally score human. Introduce sentence variety. Use unexpected but accurate word choices. Let your natural voice through instead of over-editing toward "correct."

That's exactly what GetHumanized's restructuring engine does: it adjusts perplexity and burstiness while preserving your meaning, facts, and citations.