Abstract
Prior work on AI-enabled assistive technology (AT) for people with visual impairments (VI) has treated navigation largely as an independent activity. Consequently, much effort has focused on providing individual users with wayfinding details about the environment, including information on distances, proximity, obstacles, and landmarks. However, independence is also achieved by people with VI through interacting with others, such as in collaboration with sighted guides. Drawing on the concept of interdependence, this research presents a systematic analysis of sighted guiding partnerships. Using interaction analysis as our primary mode of data analysis, we conducted an empirical, qualitative study with 4 couples, each made up of person with a vision impairment and their sighted guide. Our results show how pairs used interactional resources such as turn-taking and body movements to both co-constitute a common space for navigation, and repair moments of rupture to this space. This work is used to present an exemplary case of interdependence and draws out implications for designing AI-enabled AT that shifts the emphasis away from independent navigation, and towards the carefully coordinated actions between people navigating together.

Abstract
AI technologies are often used to aid people in performing discrete tasks with well-defined goals (e.g., recognising faces in images). Emerging technologies that provide continuous, real-time information enable more open-ended AI experiences. In partnership with a blind child, we explore the challenges and opportunities of designing human-AI interaction for a system intended to support social sensemaking. Adopting a research-through-design perspective, we reflect upon working with the uncertain capabilities of AI systems in the design of this experience. We contribute: (i) a concrete example of an open-ended AI system that enabled a blind child to extend his own capabilities; (ii) an illustration of the delta between imagined and actual use, highlighting how capabilities derive from the human-AI interaction and not the AI system alone; and (iii) a discussion of design choices to craft an ongoing human-AI interaction that addresses the challenge of uncertain outputs of AI systems.

Abstract
For individuals with mental illness, social media platforms are considered spaces for sharing and connection. However, not all expressions of mental illness are treated equally on these platforms. Different aggregates of human and technical control are used to report and ban content, accounts, and communities. Through two years of digital ethnography, including online observation and interviews, with people with eating disorders, we examine the experience of content moderation. We use a constructivist grounded theory approach to analysis that shows how practices of moderation across different platforms have particular consequences for members of marginalized groups, who are pressured to conform and compelled to resist. Above all, we argue that platform moderation is enmeshed with wider processes of conformity to specific versions of mental illness. Practices of moderation reassert certain bodies and experiences as ‘normal’ and valued, while rejecting others. At the same time, navigating and resisting these normative pressures further inscribes the marginal status of certain individuals. We discuss changes to the ways that platforms handle content related to eating disorders by drawing on the concept of multiplicity to inform design.