AI images, labels and news demand

Maja Adena, Eleonora Alabrese, Francesco Capozza and Isabelle Leader

Discussion Papers, Research Group Information, Incentives, Inequality from WZB Berlin Social Science Center

Abstract: We test whether AI-generated news images affect outlet demand and trust. In a preregistered experiment with 2,870 UK adults, the same article was paired with a wireservice photo (with/without credit) or a matched AI image (with/without label). Average newsletter demand changes little. Ex-post photo origin recollection is poor, and many believe even the real photo is synthetic. Beliefs drive behavior: thinking the image is AI cuts demand and perceived outlet quality by about 10 p.p., even when the photo is authentic; believing it is real has the opposite effect. Labels modestly reduce penalties but do little to correct mistaken attributions.

Keywords: AI; Demand for News; Trust; Online Experiment (search for similar items in EconPapers)
JEL-codes: C81 C93 D83 (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/336444/1/ii26-601.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:wzbiii:336444

Access Statistics for this paper

More papers in Discussion Papers, Research Group Information, Incentives, Inequality from WZB Berlin Social Science Center Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().