SynthesisFrontiers in artificial intelligence2026
When news is "written by artificial intelligence": a systematic review of provenance and disclosure cues in journalism and their effects on credibility and trust.
Synthesis in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Artificial intelligence (AI) is increasingly embedded in journalism, yet audience responses may depend on both AI provenance, meaning who or what is presented as having written the story, and transparency cues that disclose AI use. This systematic literature review synthesises empirical studies examining how AI provenance cues and AI disclosure cues in journalism affect perceived credibility and trust. Methods: Following PRISMA 2020 and PRISMA-S, Scopus and Web of Science Core Collection were searched on 2 February 2026 for English-language, peer-reviewed journal articles and conference papers. Searches yielded 492 records. After deduplication and pre-screen exclusions, 290 records were screened at title/abstract level, and 47 studies with retrievable full texts were included. A structured narrative synthesis was conducted, guided by the Synthesis Without Meta-analysis (SWiM) guideline, to map study designs, cue operationalisations, outcome targets (message, source, outlet), and moderators. Results: Across heterogeneous designs, AI provenance cues were not associated with a consistent "AI penalty": most extractable results indicated no difference between AI-attributed and human-attributed news, and observed effects were typically conditional on topic, baseline trust, outlet/source cues, and whether human oversight was signalled. Evidence on disclosure cues was limited (10 studies) and was dominated by null or conditional findings. Scepticism appeared more likely when disclosures implied full automation without accompanying accountability or oversight information. Discussion: A Cue-Inference-Target (CIT) framework is proposed to explain when AI cues shift epistemic-quality versus normative-legitimacy judgments. Future research should use factorial designs that separate provenance from disclosure and standardise reporting of cue wording, placement, and validated outcome measures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.