Diversity, equality, and inclusion (DEI) are critical factors that need to be considered when developing AI and robotic technologies for people. The lack of such considerations exacerbates and can also perpetuate existing forms of discrimination and biases in society for years to come. Although concerns have already been voiced around the globe, there is an urgent need to take action within the human-robot interaction (HRI) community. This workshop contributes to filling the gap by providing a platform in which to share experiences and research insights on identifying, addressing, and integrating DEI considerations in HRI. With respect to last year, this year the workshop will further engage participants on the problem of sampling biases through hands-on co-design activities for mitigating inequity and exclusion within the field of HRI.

Belonging to a group is a natural need for human beings. Being left out and rejected represents a negative event, which can cause discomfort and stress to the excluded person and other members. Social robots have been shown to have the potential to be optimal tools for studying influence in group interactions, providing valuable insights into how human group dynamics can be modeled, replicated, and leveraged. In this work, we aim to study the effect of being excluded by a social robot in a teenagers-robot interaction. We propose a conversational turn-taking game, inspired by the Cyberball paradigm and rooted in social exclusion mechanisms, to explore how the humanoid robot iCub can affect group dynamics by excluding one of the group members. Preliminary results show that the included player tries to re-engage with the one excluded by the robot. We interpret this dynamic as an included player's tentative to compensate for the exclusion and reestablish a balance, in line with findings in human-human interaction research. Furthermore, the paradigm we developed seems a suitable tool for researching social influence in different Human-Robot Interaction contexts.

This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants.

Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactful on sensitive domains such as mental health and neurological disorders, where speech data are used to develop AI applications for patients and healthcare providers. In this paper, we chart the landscape of available speech datasets for this domain, to highlight possible pitfalls and opportunities for improvement and promote fairness and diversity. We present a comprehensive list of desiderata for building speech datasets for mental health and neurological disorders and distill it into an actionable checklist focused on ethical concerns to foster more responsible research.

Diversity, equity, and inclusion (DEI) are key factors in the development of robot systems that interact with people in the real world. Without such considerations, biases toward underrepresented groups can exacerbate discrimination and perpetuate harm. The human-robot interaction (HRI) community must urgently take action to prevent this. This workshop addresses these issues by providing a forum to build community, share experiences, and disseminate research findings on DEI considerations in HRI.

With the rapid advancement of autonomous vehicle (AV) technology, AVs move beyond their initial purpose of only providing a self-driving and/or assistive driving experience, and progressively transform into interactive agents with some level of autonomy, as well as some context-dependent social features. This introduces new challenges and questions, already relevant in other areas of human-robot interaction (HRI), such as: if an AV is perceived as a social agent by the human with whom it is interacting or collaborating, how are the various facets of its interface and behaviour impacting its human partner? And how do we foster a successful collaboration between the human driver and the AV, maximizing the driver’s comfort, agency, and trust in the AV? Our specific research goal in this area is to explore how the human’s perception of the AV can vary across different levels of information sharing and autonomy of the AV. More precisely, in this work we sought to understand the various factors that could influence naïve participants’ acceptance and trustworthiness of AV. In a between-subjects online study, informed by participatory design, we investigated the effects of different AV interfaces with different information levels on participants’ perceptions of the AV, specifically their trust and comfort ratings. We also sought to understand how much the environment and the AV’s behaviour play a role in determining the level of autonomy users will grant to the AV. We found that the transparency of the AV (described by the amount of information shared with the users) had a significant impact on people’s trust and comfort in the car. Participants’ relationship with the AV’s autonomy however was more complex, and was influenced both by the AV’s transparency, but also by the driving environment and the specific events happening in the scenes.