"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.
Authors
- Asja Fischer (ORCID: https://orcid.org/0000-0002-1916-7033)
- Lea Schönherr (ORCID: https://orcid.org/0000-0003-3779-7781)
- Rebecca Weil (ORCID: https://orcid.org/0000-0002-3725-7948)
- Thorsten Holz (ORCID: https://orcid.org/0000-0002-2783-1264)
- Jonas Ricker (ORCID: https://orcid.org/0000-0002-7186-3634)
- Sascha Fahl (ORCID: https://orcid.org/0000-0002-5644-3316)
- Charlotte Schwedes
- Sandra Höltervennhoff
Institutions
- 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)
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
Funders
- Deutsche Forschungsgemeinschaft
- Volkswagen Foundation
- Bundesministerium für Bildung und Forschung
- Daimler und Benz Stiftung