On-demand, last-mile delivery -- the transportation of goods from a local distribution center to the customer's door within a specified time window -- is used by people with disabilities (PwDs) for a variety of reasons. While delivery robots have potential in this space, they often widen disparities in access for PwDs. Inspired by the concept of pluriversality, which embraces the diversity of worldviews, we facilitated participatory design workshops with PwDs to co-design accessible and equitable delivery robots. Our findings support the importance of delivery robots with varied form factors that are robust and customizable to support diverse PwDs and their respective needs and preferences. We also provide broader considerations for automation in delivery ecosystems.

This paper explores new ways to support blind and low vision (BLV) game stream participants. Prior work on game-aware streaming systems has focused on the potential for viewer interaction and personalization for sighted viewers, but how such systems impact interaction and personalization for BLV viewers remains largely unexplored. Most streaming experiences have significant visual information but no non-visual or sensemaking alternatives, which can exclude BLV viewers from understanding and interacting with the streaming experience. Building on the pre-existing system MARS, we developed a design probe that makes game data available to stream viewers in personalizable visual and non-visual formats. We use this probe to investigate the needs of BLV game stream viewers through qualitative interviews and live prototype testing sessions on Twitch. In addition to the technical contributions of our probe, our work addresses how game-aware streaming technologies can align with the needs and motivations of BLV viewers, and paves the way for novel designs in future iterations of game-aware streaming technologies.

Autistic adults often experience stigma and discrimination at work, leading them to seek social communication support from coworkers, friends, and family despite emotional risks. Large language models (LLMs) are increasingly considered an alternative. In this work, we investigate the phenomenon of LLM use by autistic adults at work and explore opportunities and risks of LLMs as a source of social communication advice. We asked 11 autistic participants to present questions about their own workplace-related social difficulties to (1) a GPT-4-based chatbot and (2) a disguised human confederate. Our evaluation shows that participants strongly preferred LLM over confederate interactions. However, a coach specializing in supporting autistic job-seekers raised concerns that the LLM was dispensing questionable advice. We highlight how this divergence in participant and practitioner attitudes reflects existing schisms in HCI on the relative privileging of end-user wants versus normative good and propose design considerations for LLMs to center autistic experiences.

Examinations of intersectionality and identity dimensions in accessibility research have primarily considered disability separately from a person’s race and ethnicity. Accessibility work often does not include considerations of race as a construct, or treats race as a shallow demographic variable, if race is mentioned at all. The lack of attention to race as a construct in accessibility research presents an oversight in our field, often systematically eliminating whole areas of need and vital perspectives from the work we do. Further, there has been little focus on the intersection of race and disability within accessibility research, and the relevance of their interplay. When research in race or disability does not mention the other, this work overlooks the potential to better understand the full nuance of marginalized and “otherized” groups. To address this gap, we present a series of case studies exploring the potential for research that lies at the intersection of race and disability. We provide examples of how to integrate racial equity perspectives into accessibility research, through positive examples found in these case studies and reflect on teaching at the intersection of race, disability, and technology. This paper highlights the value of considering how constructs of race and disability work alongside each other within accessibility research studies, designs of socio-technical systems, and education. Our analysis provides recommendations towards establishing this research direction.

Dementia affects >50 million worldwide, causing progressive cognitive and physical disabilities. Its caregiving burden falls largely onto informal caregivers, who experience their own health problems, and face tremendous stress with little support–all exacerbated during COVID-19. In this paper, we present a new caregiver support perspective, where the lenses of health equity and community health can shape future technology design. Through a 1.5 year long, in-depth research process with dementia community health workers, we learned how caregiving support technology can reflect key concepts in dementia community health practice. This paper makes two contributions: 1) We propose employing embodied cueing, such as imitation or action mimicry, as a communication modality that can align technology with community caregiving approaches, promote agency in people with dementia, and relieve caregiver burden, and 2) We suggest new avenues for HCI research to advance health equity in the context of dementia technology design.

Many new technologies are being built to support people with dementia. However, they largely focus on the people with dementia; consequently, informal caregivers, one of the most important stakeholders in dementia care, remain invisible within the technology design space. In this paper, we present a six-month long, community-based design research process where we collaborated with dementia caregiver support groups to design robots for dementia caregiving. The contributions of this paper are threefold. First, we broaden the context of dementia robot design to give a more prominent role to informal family care-givers in the co-design process. Second, we provide new design guidelines that contextualize robots within the family caregiving paradigm, which suggest new roles and behaviors of robots. These include lessening emotional labor by communicating information caregivees do not want to hear (e.g., regarding diet or medication) or providing redirection during emotionally difficult times, as well as facilitating positive shared moments. Third, our work found connections between certain robot attributes and their relationship to the stage of dementia a caregivee is experiencing. For example, caregivers wanted their robots to facilitate interaction with their caregivees in early stages of dementia, yet be in the background. However, for later stages of dementia, they wanted robots to replace caregiver-caregivee interaction to lessen their emotional burden, and be foregrounded. These connections provide important insights in to how we think about adaptability and long-term interaction in HRI. We hope our work provides new avenues for HRI researchers to studying robots for dementia caregivers by engaging in community-based design.