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Cited by 3Misinformation and Its ImpactsDigital Media Forensic DetectionSpam and Phishing Detection

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

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Höltervennhoff, Sandra, Ricker, Jonas, Raphael, Maike M., Schwedes, Charlotte, Weil, Rebecca, Fischer, Asja, Holz, Thorsten, Schönherr, Lea +1 more·2026·Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3772318.3791006
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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.

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Authors
Höltervennhoff, S., Ricker, J., Raphael, M. M., Schwedes, C., Weil, R., Fischer, A., Holz, T., Schönherr, L., & Fahl, S.
Affiliations
  • Leibniz University Hannover (DE)
  • Ruhr University Bochum (DE)
  • Helmholtz Center for Information Security (DE)
  • Max Planck Institute for Security and Privacy (DE)
ORCID
Venue
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2026-04-13
Pages
1-32
DOI
10.1145/3772318.3791006
OpenAlex
W4416604008
Cited by
3
References
0
Citations by year
2026: 2 · 2025: 1
FWCI
27.17
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • arXiv (Cornell University)
  • CISPA Helmholtz Center (cc-by)
Funders
  • VolkswagenStiftung (Niedersächsisches Vorab – ZN3695)
  • German Federal Ministry of Education and Research (SisWiss (16KIS2330))
  • Deutsche Forschungsgemeinschaft (DFG) (EXC 2092 CASA - 390781972)
  • Daimler and Benz Foundation (Ladenburger Kolleg, Project KonCheck)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
generative AI, AI disclosure, mislabeling, generative AI regulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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