Profit-oriented service sectors such as tourism, hospitality, and entertainment are increasingly looking at how professional service robots can be integrated into the workplace to perform socio-cognitive tasks that were previously reserved for humans. This is a work in which social and labor sciences recognize the principle role of emotions. However, the models and narratives of emotions that drive research, design, and deployment of service robots in human–robot interaction differ considerably from how emotions are framed in the sociology of labor and (...) feminist studies of service work. In this paper, we explore these tensions through the concepts of affective and emotional labor, and outline key insights these concepts offer for the design and evaluation of professional service robots. Taken together, an emphasis on interactionist approaches to emotions and on the demands of affective labor, leads us to argue that service employees are under-represented in existing studies in human–robot interaction. To address this, we outline how participatory design and value-sensitive design approaches can be applied as complimentary methodological frameworks that include service employees as vital stakeholders. (shrink)

To address the tension between demands for more transparent AI systems and the aim to develop and design robots with apparent agency for smooth and intuitive human-robot interaction (HRI), we present in this paper an argument for why explainability in HRI would benefit from being question-centered. First, we review how explainability has been discussed in AI and HRI respectively, to then present the challenge in HRI to accommodate the requirement of transparency while also keeping up the appearance of the robot (...) as a social agent. Based on the conceptualization of questions as information-seeking acts, our proposal for a question-centered approach to explainable HRI (xHRI) implies that to ensure transparency, robots shall provide explanations upon request in the form of questioning. We end this paper by laying out a preliminary taxonomy of xHRI-relevant questions that present the kinds of explanations a robot can or should be able to provide for successful and transparent HRI. (shrink)