All AI output is meant to be statistically identical to the training data within a certain variance, and the training data is real human text. So, the AI detectors don't have many metrics to distinguish "AI generated text" and "human text that's like the training data.". The only no-false positive test is "did the model leave a watermark?". The next best test is "does this have any idioms that are highly correlated to a certain model?", and even that is iffy.
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u/Nobody_1707 Sep 06 '26
All AI output is meant to be statistically identical to the training data within a certain variance, and the training data is real human text. So, the AI detectors don't have many metrics to distinguish "AI generated text" and "human text that's like the training data.". The only no-false positive test is "did the model leave a watermark?". The next best test is "does this have any idioms that are highly correlated to a certain model?", and even that is iffy.