Readers' perceived credibility of news stories with AI authorship and different levels of transparency
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[EMBARGOED UNTIL 05/01/2026] As newsrooms utilize more AI -- from assisting the news production to generating news stories -- readers' trust in AI-generated news must be examined. Using a 2 (authorship: AI or human) x3 (methodology transparency: absent, low, or high) mixed factorial design, this study examined the effects of authorship and methodology transparency on perceived news credibility, news transparency, source credibility, and behavioral intention. Authorship was operationalized as the byline and methodology transparency was operationalized as a yellow-highlighted paragraph explaining the reporting process. A total of 262 participants from Prolific completed the experiment in which they were asked to read three news stories, each reflecting one of the three levels of methodology transparency and one author type, and to provide their self-reported responses. The findings showed that human-authored stories resulted in significantly higher perceived news credibility and source credibility than AI-authored stories. Additionally, the high methodology transparency increased perceived news credibility, news transparency, source credibility, and behavioral intention compared to both absent and low methodology transparency conditions. The practical implications of the findings for newsrooms were discussed.
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M.A.