"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation

As generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated content. However, it is unclear if labels reduce the risk of users falling for AI-generated misinformation. To address this research gap, we study the effect of labels on users’ perception and the implications of mislabeling, focusing on AI-generated images. We first explored users’ opinions and expectations of labels using five focus groups. Although participants were wary of practical implementations, they considered labeling helpful in identifying AI-generated images and avoiding deception. Second, we conducted a survey with 1 354 participants to assess how labels affect users’ ability to recognize misinformation. While labels reduced participants’ belief in false claims supported by AI-generated images, we found evidence of overreliance, leading to unintended side effects: Participants were more susceptible to false claims accompanied by human-made images, and were more hesitant to believe true claims illustrated with labeled AI-generated images.

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Publication Details

Published
2026-04-13
DOI
https://doi.org/10.1145/3772318.3791006
Citations
1
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00

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article

"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation

Asja Fischer, Lea Schönherr, Rebecca Weil, Thorsten Holz et al.
1 citations
Misinformation and Its Impacts
article

"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation

Asja Fischer, Lea Schönherr, Rebecca Weil, Thorsten Holz, Jonas Ricker, Sascha Fahl, Charlotte Schwedes, Sandra Höltervennhoff
article en
1 citations

Abstract

As generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated content. However, it is unclear if labels reduce the risk of users falling for AI-generated misinformation. To address this research gap, we study the effect of labels on users’ perception and the implications of mislabeling, focusing on AI-generated images. We first explored users’ opinions and expectations of labels using five focus groups. Although participants were wary of practical implementations, they considered labeling helpful in identifying AI-generated images and avoiding deception. Second, we conducted a survey with 1 354 participants to assess how labels affect users’ ability to recognize misinformation. While labels reduced participants’ belief in false claims supported by AI-generated images, we found evidence of overreliance, leading to unintended side effects: Participants were more susceptible to false claims accompanied by human-made images, and were more hesitant to believe true claims illustrated with labeled AI-generated images.

Leibniz University Hannover (DE), Max Planck Institute for Security and Privacy (DE), Helmholtz Center for Information Security (DE), Hannover Re (Germany) (DE), Ruhr University Bochum (DE)
Deutsche Forschungsgemeinschaft, Volkswagen Foundation, Bundesministerium für Bildung und Forschung, Daimler und Benz Stiftung
Openalex Percentile: Top 100%
Misinformation and Its Impacts
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