The advancement of AI is driving the integration of robots into everyday environments. The acceptance of these robots not only depends on direct users, but also on others who share these spaces, often referred to as bystanders or non-users. A frequently discussed prerequisite for acceptance is the proper handling of personal information as robots are equipped with means for environmental awareness, data inference, and human interaction. Although bystanders are not the main target of such processing, they can be affected by robot operation. Despite its significance, bystander privacy concerns have received limited attention in prior robotics research. In this paper, we address bystander privacy in the context of robots operating in everyday environments. We conduct a scoping review of bystander privacy issues associated with related technologies exhibiting agentic qualities comparable to robots. We analyze how agency may reshape conventional attributions of actor roles and transmission principles within the established privacy framework of Contextual Integrity. This allows us to derive transferable insights about privacy expectations as well as unique opportunities and open research issues for robots in public spaces.

Many human activities are performed in groups—making decisions in workplace meetings, cooperating on a sports team, or meeting with friends for dinner. All these activities involve complex conditions and interaction processes that influence their outcomes in terms of performance, personal goals, and group objectives. As robots are increasingly being positioned within groups, improving these outcomes has emerged as an important application area in social robotics, particularly through robotic facilitation. Robot facilitators aim to elicit positive changes by deliberately influencing group processes. While research in this field has demonstrated that robots can effectively influence interpersonal dynamics, there remains a notable gap in consolidating these insights into a coherent understanding that can guide the design and development of better facilitators. We present a scoping review of literature targeting changes in interactions between multiple humans that are driven by intentional actions from robotic agents. To identify key considerations for the design of robot facilitators, we take inspiration from human group research theories to organize existing approaches. Our review includes 108 publications that meet our inclusion criteria, yielding 85 distinct application targets for group facilitation using robots. Based on the identified instances, we extract categories of possible application targets and a set of design concepts that can guide future work on robotic group facilitators.

Privacy is an important yet understudied focus in consideration of successful human-robot interaction (HRI). In this paper, we present a thematic analysis of the topics discussed in the Privacy-Aware Robotics Workshop held at the HRI Conference in 2024. The analysis points across the perspectives of User, Engineering, and Society with particular identification of open “interdisciplinary zones” at the intersections of these perspectives. Based on that, we formulate and present three main themes for future research directions: the tension between robot functioning and effectiveness of privacy implementations in real-world contexts; the need to involve and empower target user communities in the co-design of privacy-aware robots, and the consideration of regulatory frameworks that extend across jurisdictions in robot design.

Social robots are increasingly recognized as valuable supporters in the field of well-being coaching. They can function as independent coaches or provide support alongside human coaches, and healthcare professionals. In coaching interactions, these robots often handle sensitive information shared by users, making privacy a relevant issue. Despite this, little is known about the factors that shape users’ privacy perceptions. This research aims to examine three key factors systematically: (1) the transparency about information usage, (2) the level of specific user control over how the robot uses their information, and (3) the robot’s behavioral approach – whether it acts proactively or only responds on demand. Our results from an online study (N = 200) show that even when users grant the robot general access to personal data, they additionally expect the ability to explicitly control how that information is interpreted and shared during sessions. Experimental conditions that provided such control received significantly higher ratings for perceived privacy appropriateness and trust. Compared to user control, the effects of transparency and proactivity on privacy appropriateness perception were low, and we found no significant impact. The results suggest that merely informing users or proactive sharing is insufficient without accompanying user control. These insights underscore the need for further research on mechanisms that allow users to manage robots’ information processing and sharing, especially when social robots take on more proactive roles alongside humans.

The physical activities we perform throughout our daily lives tell a great deal about our goals, routines, and behavior, and as such, have been known for a while to be a key indicator for psychiatric disorders. This paper focuses on the use of a wrist-watch with integrated inertial sensors. The algorithms that deal with the data from these sensors can automatically detect the activities that the patient performed from characteristic motion patterns. Such a system can be deployed for several weeks continuously and can thus provide the consulting psychiatrist an insight in their patient's behavior and changes thereof. Since these algorithms will never be flawless, however, a remaining question is how we can support the psychiatrist in assigning confidence to these automatic detections. To this end, we present a study where visualizations at three levels from a detection algorithm are used as feedback, and examine which of these are the most helpful in conveying what activities the patient has performed. Results show that just visualizing the classifier's output performs the best, but that user's confidence in these automated predictions can be boosted significantly by visualizing earlier pre-processing steps.