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AI Images, Labels and News Demand

Author

Listed:
  • Maja Adena
  • Eleonora Alabrese
  • Francesco Capozza
  • Isabelle Leader

Abstract

We test whether AI-generated news images affect outlet demand and trust. In a pre-registered experiment with 2,870 UK adults, the same article was paired with a wire-service 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.

Suggested Citation

  • Maja Adena & Eleonora Alabrese & Francesco Capozza & Isabelle Leader, 2025. "AI Images, Labels and News Demand," CESifo Working Paper Series 12277, CESifo.
    • Handle: RePEc:ces:ceswps:_12277
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    Keywords

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    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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