While human-like social interactions can enhance trust in and acceptance of automated vehicles (AVs), overuse may hinder these benefits, reflecting the “uncanny valley of mind” effect. We hypothesized that the AV agent’s human-like features—calling drivers by their name (Name) and expressing emotions (Emotion)—enhance trust and acceptance individually but may have adverse effects when combined. A 2 × 2 between-subjects experiment (N = 84) examined these effects. Participants in the Name and Emotion combination were more likely to perceive the experiential mind in the AV compared to the Name or Emotion conditions. However, they were less likely to show behavioral trust in the AV than in the Emotion condition, to perceive the AV as useful than in either the Name or Emotion condition, and to show intention to use the AV than in the Name condition. These findings highlight potential trade-offs in designing social AV interactions.

In avatar-mediated communications, users often cannot identify how others’ avatars are created, which is one of the important information they need to evaluate others. Thus, we tested a social virtual world that is transparent about others’ avatar-creation methods and investigated how knowing about others’ avatar-creation methods shapes users’ perceptions of others and their self-disclosure. We conducted a 2x2 mixed-design experiment with system design (nontransparent vs. transparent system) as a between-subjects and avatar-creation method (customized vs. personalized avatar) as a within-subjects variable with 60 participants. The results revealed that personalized avatars in the transparent system were viewed less positively than customized avatars in the transparent system or avatars in the nontransparent system. These avatars appeared less comfortable and honest in their self-disclosure and less competent. Interestingly, avatars in the nontransparent system attracted more followers. Our results suggest being cautious when creating a social virtual world that discloses the avatar-creation process.

Artificial intelligence (AI) applications have become an integral part of our society. However, studying AI as one entity or studying idiosyncratic applications separately both have limitations. Thus, this study used computational methods to categorize ten different AI roles prevalent in our everyday life and compared laypeople’s perceptions of them using online survey data (N = 727). Based on theoretical factors related to the fundamental nature of AI, the principal component analysis revealed two dimensions that categorize AI: human involvement and AI autonomy. K-means clustering identified four AI role clusters: tools (low in both dimensions), servants (high human involvement and low AI autonomy), assistants (low human involvement and high AI autonomy), and mediators (high in both dimensions). Multivariate analyses of covariances revealed that people assessed AI mediators the most and AI tools the least favorably. Demographics also influenced laypeople’s assessments of AI. The implications of these results are discussed.

Trust plays an essential role in the interaction between humans and artificial intelligence (AI). To promote trust in AI, information about the AI’s performance should be communicated well to the users. Accordingly, this paper investigates how information about AI performance should be presented, focusing on message framing and the ownership of decisions. A 2 (ownership: no ownership vs. ownership) × 3 (message framing: no information vs. negative information vs. positive information) between-subjects experiment was conducted (N = 120). Participants were asked to choose items to help them survive in the desert, supported by an AI decision. The results showed that participants without decision ownership perceived higher trust than those with decision ownership. Also, trust was perceived to be higher when participants were not given performance information than when they were. The results indicate the importance of carefully communicating with AI. The implications of this study are discussed.