Why do fake news detectors flag AI-generated truthful content?

Fake news detectors are trained to identify deceptive content. But when LLM-generated text enters the ecosystem, these detectors develop an unexpected bias: they are more prone to flagging LLM-generated content as fake news while often misclassifying human-written fake news as genuine.

The mechanism is a confound between AI linguistic style and deception signals. LLM-generated text has distinct linguistic patterns — Can human judges detect measurable differences in AI text? — and these patterns happen to overlap with signals that fake news detectors use to identify deception. The detectors are not evaluating veracity; they are detecting a style that correlates with their training distribution of "fake."

This creates a double failure:

1. False positives on AI-generated truthful content — genuine information written or paraphrased by AI gets flagged
2. False negatives on human-written disinformation — actual fake news passes because it has human linguistic patterns

The proposed mitigation — adversarial training with LLM-paraphrased genuine news — teaches detectors to disentangle style from content. But the deeper issue persists: any detection system trained on historical corpora of human deception will be confounded by the introduction of a new text source (LLMs) whose linguistic properties are orthogonal to the deception dimension.

This extends the measurably-non-human finding to a practical consequence. The same linguistic distinctiveness that makes LLM text statistically identifiable also makes it systematically misclassified by tools designed for a different task. The pattern is: build a detector on one signal (deception), deploy it in an environment where a new signal (AI authorship) correlates with the training distribution → systematic bias.