Systematic Review of AI Provenance and Disclosure Cues in Journalism

Lorena Licenji's systematic literature review synthesizes 47 empirical studies on how AI provenance and disclosure cues in news affect perceived credibility and trust. The dataset, last updated on 2026-05-05, includes results from a search of Scopus and Web of Science that yielded 492 records. It provides a structured narrative synthesis guided by the Synthesis Without Meta-analysis (SWiM) guideline.

Use Cases

• Analyze the relationship between AI attribution and perceived message credibility based on the review's findings.
• Study the conditional effects of topic and baseline trust on audience responses to AI cues based on the described results.
• Investigate the role of human oversight signaling in mitigating audience skepticism based on the discussion.
• Design factorial experiments separating provenance from disclosure cues based on the review's proposed framework.

Strengths

• Follows PRISMA 2020 and PRISMA-S systematic review protocols.
• Synthesizes findings from 47 included studies after screening 290 records.
• Provides a structured narrative synthesis guided by the SWiM guideline.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small (155.5 KB), indicating limited scope.

Provenance

Source

Lorena Licenji

Collection Method

Systematic literature review of Scopus and Web of Science Core Collection.

Freshness

Last updated 2026-05-05 05:24:45; freshness should be verified.