Critical voices within and beyond the scientific community have pointed to a grave matter of concern regarding who is included in research and who is not. Subsequent investigations have revealed an extensive form of sampling bias across a broad range of disciplines that conduct human subjects research called “WEIRD”: Western, Educated, Industrial, Rich, and Democratic. Recent work has indicated that this pattern exists within human–computer interaction (HCI) research, as well. How then does human–robot interaction (HRI) fare? And could there be other patterns of sampling bias at play, perhaps those especially relevant to this field of study? We conducted a systematic review of the premier ACM/IEEE International Conference on Human-Robot Interaction (2006–2022) to discover whether and how WEIRD HRI research is. Importantly, we expanded our purview to other factors of representation highlighted by critical work on inclusion and intersectionality as potentially underreported, overlooked, and even marginalized factors of human diversity. Findings from 827 studies across 749 papers confirm that participants in HRI research also tend to be drawn from WEIRD populations. Moreover, we find evidence of limited, obscured, and possible misrepresentation in participant sampling and reporting along key axes of diversity: sex and gender, race and ethnicity, age, sexuality and family configuration, disability, body type, ideology, and domain expertise. We discuss methodological and ethical implications for recruitment, analysis, and reporting, as well as the significance for HRI as a base of knowledge.

Social robots have great potential in supporting individuals’ physical and mental health/wellbeing. While they have been increasingly evaluated in some domains, such as with children with autism, their evaluation has not been as extensive in other areas. We present a systematic review of domains in which social robots have been evaluated specifically in health/wellbeing contexts. We ask which robots have been evaluated, who the participants were, and how participants interacted with the robots. PRISMA guidelines for systematic reviews were followed. Articles with children as participants, using a purely robotic device, and in languages other than English were excluded. A total of 9,362 peer-reviewed articles (up to February 2021) from ACM DL, IEEE Xplore, Scopus, PubMed, and PsychInfo were identified. After applying the inclusion/exclusion criteria 443 articles were included in the review. The majority of studies were conducted at care centers while studies in hospitals/clinics have seen relatively limited attention. In many cases, the social robots were not programmed for specific health-related tasks, limiting their application. We also discuss robots used in real-world settings and propose a “Personal healthcare journey,” which includes different stages of one’s life which could benefit from a social robot, with the goal of increasing long-term adoption of social robots for supporting health/wellbeing.

The inclusion of technologies such as telepractice, and virtual reality in the field of communication disorders has transformed the approach to providing healthcare. This research article proposes the employment of similar advanced technology – social robots, by providing a context and scenarios for potential implementation of social robots as supplements to stuttering intervention. The use of social robots has shown potential benefits for all the age group in the field of healthcare. However, such robots have not yet been leveraged to aid people with stuttering. We offer eight scenarios involving social robots that can be adapted for stuttering intervention with children and adults. The scenarios in this article were designed by human–robot interaction (HRI) and stuttering researchers and revised according to feedback from speech-language pathologists (SLPs). The scenarios specify extensive details that are amenable to clinical research. A general overview of stuttering, technologies used in stuttering therapy, and social robots in health care is provided as context for treatment scenarios supported by social robots. We propose that existing stuttering interventions can be enhanced by placing state-of-the-art social robots as tools in the hands of practitioners, caregivers, and clinical scientists.

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.