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.

More and more immersive environments support third-party applications, leading to concerns about the trustworthiness of user interfaces (UIs), which attackers could exploit, endangering users. Although security warnings attempt to safeguard users by highlighting risks, most studies primarily target security indicators as usable intervention for app transitions in virtual reality. Our research broadens this focus by providing a systematic, data-driven investigation of security warnings for third-party applications in immersive environments. We analyzed a decade’s worth of top VR interactions and security conference findings and assessed the top 10 free VR applications from two leading stores each. From our design process, we implemented four warnings. Through two user studies involving 61 participants, we measured their responses to these warnings during virtual object interactions. Our findings indicate that a red glow on an object was the most effective warning, frequently associated with danger, while pop-up warnings were the least effective.

Researchers invested enormous efforts to understand and mitigate the concerns of users as technologies collect their private data. However, users often undermine other people’s privacy when, e.g., posting other people’s photos online, granting mobile applications to access contacts, or using technologies that continuously sense the surrounding. Research to understand technology adoption and behaviors related to collecting and sharing data about non-users has been severely lacking. An essential step to progress in this direction is to identify and quantify factors that affect technology’s use. Toward this goal, we propose and validate a psychometric scale to measure how much an individual values other people’s privacy. We theoretically grounded the appropriateness and relevance of the construct and empirically demonstrated the scale’s internal consistency and validity. This scale will advance the field by enabling researchers to predict behaviors, design adaptive privacy-enhancing technologies, and develop interventions to raise awareness and mitigate privacy risks.