How Accurate Are AI Detectors?
Short answer: no AI detector — including the free detector on this site — is fully accurate, and any tool that claims otherwise should be treated with suspicion. AI detectors are probabilistic instruments. They estimate how likely it is that a piece of text was machine-generated based on statistical patterns; they do not, and cannot, observe who actually typed the words.
What "accuracy" actually means for a detector
When people ask whether a detector is accurate, they are really asking about two different kinds of mistakes:
- False positives: human-written text that gets flagged as AI-generated.
- False negatives: AI-generated text that passes as human-written.
Every detector trades these off against each other. A tool tuned to catch more AI text will inevitably flag more human writers by mistake, and a tool tuned to protect human writers will let more AI text through. There is no setting that eliminates both.
Why published accuracy numbers are hard to trust
You may see detection tools advertise specific accuracy percentages. Treat those numbers cautiously, for a few reasons:
- Test sets go stale. A benchmark built on last year's AI models says little about text from this year's models, which write differently.
- Results depend heavily on the text. Accuracy on long, unedited AI essays tells you nothing about short answers, edited drafts, or mixed human-and-AI writing.
- Vendors choose their own tests. A company reporting its own benchmark has every incentive to pick favorable conditions.
This site does not publish an accuracy percentage because we do not have independently verified evidence to support one — and we would rather say that plainly than invent a number.
What detectors actually measure
Most detectors, including this one, look at statistical properties of the writing itself, such as:
- Sentence uniformity: AI text often keeps sentence lengths within a narrow band, while human writing swings between short and long sentences.
- Vocabulary predictability: language models tend to choose likely, common words; humans wander off-script more.
- Burstiness: the natural unevenness of human rhythm and phrasing.
- Transition-word density: generated text leans on connectors like "furthermore" and "in conclusion" more heavily than most people do.
These are real signals, but they are indirect. A careful human writer can produce very uniform, formal prose, and an edited AI draft can look quite human. That gap between the signal and the truth is exactly where detection errors live. For a closer look at the human side of that gap, see why AI detectors give false positives.
What affects the reliability of a result
- Length: very short texts give detectors little to work with, so scores on a paragraph are much less meaningful than scores on several pages.
- Editing: AI text that a human has substantially rewritten sits between the two categories and is genuinely ambiguous.
- Writing style: formulaic genres — reports, abstracts, standardized essays — resemble AI output by design.
- Language background: writing from non-native speakers is disproportionately flagged by many detectors, an important fairness concern.
How to read a detector score
Treat a score as one signal among several, never as proof. A high score is a reason to look more closely — at drafts, revision history, the writer's other work, or a conversation with the author — not a verdict on its own. A low score, likewise, does not certify that a text is human. No score from any detector is suitable, by itself, as the basis for an academic, employment, or legal decision. We cover this in more depth in how to use AI detectors responsibly.