Trust is a fundamental part of interpersonal relationships and due to the rise of AI systems in many domains, we must examine their trustworthiness. While it is possible to rely on AI to perform certain tasks in certain situations, they do not warrant interpersonal trust. An AI can only be considered trustworthy if it is normatively or affectively motivated to act as expected. We present the argument that the absence of genuine emotions in current AI systems renders them untrustworthy, even if they appear emotional by exhibiting social cues.

While the accumulation of practical knowledge provided researchers with much insight into successful human-robot interaction (HRI), a broader discussion about the role of theoretical knowledge is still lacking. It is unfortunate because it is also important to explicitly consider theory and theorizing in HRI as crucial contributions when aspiring to develop this field of research into a mature science. With our proposed interactive half-day workshop, we aim to provide a vibrant setting for the participants to discuss the “what, why, and how” of theoretical knowledge in HRI, as they share and learn from each other's experiences and competence. In the long-term perspective, the outcome of this workshop will lay the foundation for a supportive research community that encourages researchers to reflect and collaborate further on theory-grounded HRI work.

Focusing on human experience of vulnerability in everyday life interaction scenarios is still a novel approach. So far, only a proof-of-concept online study has been conducted, and to extend this work, we present a follow-up online study. We consider in more detail how human experience of vulnerability caused by a trust violation through a privacy breach affects trust ratings in an interaction scenario with the PEPPER robot assisting with clothes shopping. We report the results from 32 survey responses and 11 semi-structured interviews. Our findings reveal the existence of the privacy paradox also for studying trust in HRI, which is a common observation describing a discrepancy between the stated privacy concerns by people and their behavior to safeguard it. Moreover, we reflect that participants considered only the added value of utility and entertainment when deciding whether or not to interact with the robot again, but not the privacy breach. We conclude that people might tolerate an untrustworthy robot even when they are feeling vulnerable in the everyday life situation of clothes shopping.

Ensuring trust in human-robot interaction (HRI) is considered essential for widespread use of robots in society and everyday life. While the majority of studies use game-based and high-risk scenarios with low familiarity to gain a deeper understanding of human trust in robots, scenarios with more subtle trust violations that could happen in everyday life situations are less often considered. In this paper, we present a theory-driven approach to studying the situated trust in HRI by focusing on the experience of vulnerability. Focusing on vulnerability not only challenges previous work on trust in HRI from a theoretical perspective, but is also useful for guiding empirical investigations. As a first proof-of-concept study, we conducted an interactive online survey that demonstrates that it is possible to measure human experience of vulnerability in the ordinary, mundane, and familiar situation of clothes shopping. We conclude that the inclusion of subtle trust violation scenarios occurring in the everyday life situation of clothes shopping enables a better understanding of situated trust in HRI, which is of special importance when considering more near-future applications of robots.

Following affective turn in cognitive science, recent decades have witnessed an increasing interest toward the role of emotions in education. Ample evidence suggests that learners and teachers experience a variety of emotions, ranging from joy and pride to anger and frustration. However, when it comes to the design of affective behavior in robotic systems for education purposes, the emphasis has been predominantly on communication of positive emotions. While we recognize that positive emotions are fundamental to successful learning, in this paper we wish to make the case for the consideration of ambivalent emotions for the design of social robots for tutoring. To ground this proposal, we focus on the emotion of teachers' disappointment. First, we discuss under which conditions communicated teachers' disappointment, while it may be experienced as emotionally ambivalent by teachers and students, functions as an affiliating pedagogical strategy. We proceed to sketch out the methodological suggestions we consider relevant for future studies of communicated disappointment in human-robot interactions within learning contexts. We conclude with critical reflections about the ethics of responsible designs of such studies.