AI detectors look for statistical patterns in text that differ between typical human writing and typical AI output. They then estimate a probability that the text came from an AI model instead of a person.
Core idea
Most detectors are themselves machine‑learning models trained on large sets of human‑written and AI‑generated text. During training, they learn which linguistic features are more common in each group and use this to classify new text as “likely human” or “likely AI.”
Key signals in the text
Detectors usually analyze several characteristics at once. Common ones include:
- Predictability of word choices, often measured with metrics like perplexity (very predictable text tends to look more like AI).
- Variation in sentence length and style (“burstiness”); human writing usually has more irregular rhythm than AI.
- Repetition, overly consistent tone, and very uniform structure, which can signal machine‑generated language.
How the model makes a decision
Under the hood, many detectors use classifiers built on neural networks or transformer models, similar to those used to generate AI text. The classifier outputs a score or label (for example, a percentage likelihood that the text is AI‑written), which tools then present as a detection result.
Extra clues: watermarks and metadata
Some systems add hidden “watermarks” or markers to AI outputs that specialized tools can look for. Detectors may also examine metadata or compare text against databases of known AI outputs, though this is less common and less reliable on its own.
Limits and accuracy issues
Detection is probabilistic, not certain, so false positives (human text flagged as AI) and false negatives (AI text missed by the detector) are common, especially with short or heavily edited text. Newer, more human‑like models and post‑editing by people make reliable detection increasingly difficult over time.
