How AI Detection Really Works
Clear, sourced explanations of what AI detectors measure, how often they are wrong, and what a score can and can’t tell you.
What Is an AI Detector?
An AI detector is software that estimates how likely it is that a piece of content was produced by an AI system. It looks for statistical patterns, not evidence of who actually wrote the text, so its answer is always a probability.
Read more →Under the hoodHow AI Detectors Work
Detectors measure how predictable text is, how much it varies, and how closely it matches examples of AI writing. Each method has blind spots, which is why detectors disagree and make mistakes.
Read more →The honest answerHow Accurate Are AI Detectors?
Good detectors catch much unedited AI text, but none are accurate enough to prove misconduct. Error rates rise with edited text, short samples, and certain human writing styles, and even small false positive rates add up to many wrongly flagged people.
Read more →When human writing looks like AIAI Detector False Positives: Who Gets Flagged and Why
Detectors flag writing that is predictable, and some human writing is very predictable. Non-native English speakers, formal writers, and people writing in structured genres are flagged far more often than others.
Read more →A balanced lookAI Detection: Pros and Cons
AI detectors are useful for spotting text that deserves a closer look and for deterring careless misuse. They are harmful when treated as proof, because their errors fall on real people.
Read more →Guessing vs. evidenceAI Detection vs. Proof of Authorship
Detection examines finished text and guesses. Proof of authorship records how text was created and shows it. Detection is fast and works on anything; proof is far stronger but must be in place while the writing happens.
Read more →Marks built in at the sourceAI Watermarking and Content Credentials Explained
A watermark is a hidden signal an AI system adds to its own output; content credentials are signed records attached to a file describing how it was made. Both can confirm AI involvement when present, but their absence proves nothing.
Read more →Beyond textDetecting AI Images, Audio, and Video
Visual “tells” like strange hands are no longer reliable. The best checks are provenance (where did it come from?), content credentials when present, and confirming through a separate channel, especially for urgent requests by voice or video.
Read more →The arms raceCan AI Detection Be Beaten? Paraphrasers and Humanizers
Paraphrasing and humanizer tools can lower many detectors’ scores, but the best detectors increasingly catch rewritten text, and the rewriting often damages the writing. Process-based proof can’t be fixed by rewording at all.
Read more →How we got hereA Short History of AI Text Detection
AI text detection went from a research curiosity to a classroom staple almost overnight in 2023, hit serious accuracy and fairness problems, and is now splitting into better detectors on one side and process-based proof of authorship on the other.
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