How accurate are AI detectors?
There is no single accuracy percentage that describes every detector, input and use case. A useful result must be interpreted alongside the tool’s evaluation methods and the content’s context.
Updated October 28, 2026 · AI Checker
False positives and false negatives
A false positive flags human-created content as AI-generated. A false negative misses generated content. Both matter: an incorrect flag can lead to an unfair accusation, while a missed detection can create false reassurance.
OpenAI’s guidance for educators describes limitations observed in its own detector research, including human writing being flagged. That guidance is not a benchmark of our detection service or this product.
A signal score is not a probability
AI Checker’s live reports subtract the provider’s human score from 100 to display an AI signal. We label 80 or above likely AI-generated, 25 or below low signal, and the middle range inconclusive. These are product thresholds, not validated probabilities of authorship. An 80 signal does not mean that 80% of the text was generated.
Demo scores are simulated. They demonstrate the report interface and cannot be used to evaluate detection performance.
Why one headline percentage can mislead
Consider an illustrative test with 90 human samples and 10 generated samples. A tool that labels everything human would be 90% correct overall while missing every generated sample. This is a numerical example, not an evaluation of any named detector.
Ask for false-positive and false-negative rates, the number of samples and how the test set was assembled. Results on one language, generator or content type do not establish performance on another.
Edited and mixed content needs careful review
A document may combine human writing, generated passages and later edits. An image may combine a camera original with generated or retouched details. A single overall label cannot explain every part of that history. Passage-level signals or image metadata can add context, but neither replaces the original source.
What a useful benchmark should report
Before trusting a performance claim, look for the detector version, test date, sample counts, known origins, languages, generators, input lengths and any editing or compression. Human, generated and mixed examples should be described separately. The evaluation should state its thresholds and report inconclusive outputs rather than silently removing them.
AI Checker has not published an independent accuracy benchmark. Live text and image scans use our detection service; we do not claim a measured accuracy percentage for this interface or promise to identify a particular generator.
What to do with an uncertain result
For writing, review drafting history, notes, citations and the author’s explanation. For images, check the original file, publisher and earlier appearances. Record uncertainty when the evidence conflicts. A detector score alone should not decide academic misconduct, employment decisions or other consequential outcomes.
Try the AI text detector or AI image detector. For a step-by-step image workflow, read how to check whether an image is AI generated.