How AI detectors work

AI detectors estimate whether writing resembles text generated by a language model. They analyze the supplied text for statistical or learned patterns. A label is a classification, rather than a record of how the document was written. It does not establish accuracy, plagiarism or whether a writer followed a policy on AI assistance.

Signals behind the classification

Perplexity describes how surprising a sequence of words is to a model. Predictable wording has lower perplexity. Burstiness describes variation, such as differences in sentence length or predictability across a passage. These concepts help explain some detection methods, but modern detectors can use trained classifiers that combine many learned features. They are not simply searching for a list of banned words.

What the main detectors describe publicly

• GPTZero: Its early detector used perplexity and burstiness. Its public guidance says it moved to a deep learning architecture. The GPTZero technical report describes a trained system with predictions at different levels of a text.
• Pangram: Its published training description uses human and AI-generated examples, then adds difficult human examples that the classifier misidentified to help reduce false positives.
• Turnitin: Its AI writing report identifies qualifying prose that its model considers likely AI-generated. Its guidance warns that human and AI writing can be misidentified and calls for human judgment before adverse action. AI detection is separate from its similarity report.
• Originality: Its public explanation describes trained models using human, generated and mixed-content examples, with document analysis and sentence highlighting.
• Copyleaks: Its published FAQ describes identifying statistical patterns and sentence irregularities through comparison with human writing. Its output is a model assessment of the text.

These are broad descriptions of the providers’ published methods, not a comparison of accuracy. Their models and thresholds change. A score can mean different things across tools, so read the relevant report’s explanation before treating scores as comparable.

Why human writing gets flagged

A false positive labels human writing as AI-generated. Human and generated writing can share predictable vocabulary, formulaic structure and repeated patterns. Short passages offer less evidence. A classifier trained on particular kinds of writing may also struggle with unfamiliar styles. None of these features proves that a person used AI.

A study of non-native English writing found misclassification in the detectors it tested. That finding concerns those tools and samples, rather than establishing the behavior of every current detector. A separate evaluation of detection tools documented reliability limits. Keep drafts and sources when explaining authorship; a score alone leaves out that history.

Rewriting machine-written patterns

Ink’s AI humanizer rewrites the patterns that make text read as machine-written and that AI detectors look for: stock phrases, uniform sentence rhythm, generic transitions and filler. It aims to make text read as human-written. Results vary by detector and by text, so check your own text. Ink never sends your text to a detector and runs no automated detector checks. Read the limits of undetectable AI writing and the bypass AI detection guide. The AI phrase checker only highlights phrases for review; it cannot determine authorship.

See plans and pricing. Eligible accounts get a 7-day trial with a card required, capped at 10,000 input words. It converts to the chosen paid plan unless canceled.