Media representations of people with disabilities profoundly influence societal perceptions, yet have historically been absent, stereotyped, or inaccurate. As AI-generated visual media becomes increasingly prevalent, there is a critical opportunity to address these misrepresentations. Responding to the lack of collectively negotiated representation standards, this paper presents our human-centric approach to engaging disability communities meaningfully in AI data practices. Over three months, we worked closely with three disability organizations across the Global North and South to develop the Community Library Creator that introduces design scaffolds to support communities in defining ‘good’ representation and curating community-centric AI datasets; laying the foundations for community-specific evaluation metrics and future model adaptations. We contribute qualitative insights into the complexities of community-led data curation; discuss the value and practical challenges of intersecting human insights with AI requirements; and reflect on human-centered AI approaches that empower communities to share their perspectives and actively shape AI data practices.

The opportunity for artificial intelligence, or AI, to enable accessibility is rapidly growing, but widely impactful applications can be challenging to build given the diversity of user need within and across disability communities. Teachable AI systems give users with disabilities a way to leverage the power of AI to personalize applications for their own specific needs, as long as the effort of providing examples is balanced with the benefit of the personalization received. As an example, this paper presents the design and evaluation of Find My Things, an end-to-end application that can be taught by people who are blind or low vision to find their personal things. Through synthesis of the design process, this paper offers design considerations for the teaching loop that is so critical to realizing the power of teachable AI for accessibility.

Recent advances in AI and machine learning (ML) promise significant transformations in the future delivery of healthcare. Despite a surge in research and development, few works have moved beyond demonstrations of technical feasibility and algorithmic performance. However, to realize many of the ambitious visions for how AI can contribute to clinical impact requires the closer design and study of AI tools or interventions within specific health and care contexts. This article outlines our collaborative, human-centered approach to developing an AI application that predicts treatment outcomes for patients who are receiving human-supported, internet-delivered Cognitive Behavioral Therapy (iCBT) for symptoms of depression and anxiety. Intersecting the fields of HCI, AI, and healthcare, we describe how we addressed the specific challenges of (1) identifying clinically relevant AI applications ; and (2) designing AI applications for sensitive use contexts like mental health. Aiming to better assist the work practices of iCBT supporters, we share how learnings from an interview study with 15 iCBT supporters surfaced their practices and information needs and revealed new opportunities for the use of AI. Combined with insights from the clinical literature and technical feasibility constraints, this led to the development of two clinical outcome prediction models. To clarify their potential utility for use in practice, we conducted 13 design sessions with iCBT supporters that utilized interface mock-ups to concretize the AI output and derive additional design requirements. Our findings demonstrate how design choices can impact interpretations of the AI predictions as well as supporter motivation and sense of agency. We detail how this analysis and the design principles derived from it enabled the integration of the prediction models into a production interface. Reporting on identified risks of over-reliance on AI outputs and needs for balanced information assessment and preservation of a focus on individualized care, we discuss and reflect on what constitutes a responsible, human-centered approach to AI design in this healthcare context.

Novel AI-infused educational technologies can give children with blindness the opportunity to explore concepts learned incidentally through vision by using alternative perceptual modalities. However, more effort is needed to support the meaningful use of such technological innovations for evaluations at scale and later wide-spread adoption. This paper presents the development and pilot evaluation of a curriculum to enable educators to support blind learners’ self-exploration of social attention using the PeopleLens technology. We reflect on these learnings to present four design guidelines for creating curricula aimed to enable meaningful use. We then consider how formulations of “success” by our participants can help us think about ways of assessing efficacy in low-incidence disability groups. We conclude by arguing for our community to widen the scope of discourse around assistive technologies from design and engineering to include supporting their meaningful use.

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.