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Charlotte Schwedes

dblp:266/6913 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-2554-208XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Web and mobile security · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Web and mobile security
misinformation
1.012026
"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation · CHI 2026

Methods — techniques the papers use, named apart from their topics

survey · 1.0focus groups · 1.0
YearPublicationVenuePosition
2026 "That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation
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.
Sandra Höltervennhoff, Jonas Ricker, Maike M. Raphael, Charlotte Schwedes, Rebecca Weil, Asja Fischer, Thorsten Holz, Lea Schönherr, Sascha Fahl
CHI4