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.

In the coming years, human-AI cooperation will become an even more central part of many people’s private and work lives, supporting and shaping cognitive processes, while also influencing social dynamics. To understand the conditions under which humans choose to cooperate with AI, researchers have widely utilized evolutionary theories and game-theoretic approaches. However, these frameworks primarily emphasize utility maximization and strategic behavior, overlooking the subjective, experiential dimension of cooperation. To overcome this limitation, we here propose an integrated framework for the emergence of human-AI cooperation, which combines a mechanistic layer drawn from evolutionary theories of cooperation with an experiential layer provided by self-determination theory. We further propose to operationalize the human cooperation intent as the perceived balance between benefits and costs of cooperating, which is moderated by the extent to which psychological needs are satisfied through the cooperation. Our framework offers a novel approach for experimentally testing the formation a human-AI cooperation intent, highlighting not only when and why cooperation may occur, but also how it can be designed to be intrinsically motivating and meaningful for users.

This workshop theme centers on the development of AI agents and systems that are capable of understanding, adapting to, and reacting to collaborate with humans in compliance with the social norms. These systems leverage insights from social psychology, cognitive science, robotics, and AI to interpret social cues, anticipate the needs of others, and coordinate actions effectively within dynamic and often unpredictable contexts. We focus on embedding social awareness into AI systems, leading to Cooperative Intelligence [23] which focuses on building trust and relationship between humans and intelligent systems, instead of focusing on functions to replace humans. This paradigm is expected to realize a hybrid society, where humans coexist with ubiquitous intelligent agents.

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.