Companies across a wide range of industries are integrating new hiring technologies, including AI-powered and other automated employment decision systems, into various stages of the hiring process. Although proponents argue that these technologies can help identify suitable candidates and reduce bias, researchers and advocates have identified ethical and legal risks, including discriminatory impacts on members of marginalized groups. This work examines the impacts of "digitized assessments," commonly used by employers, on disabled workers in the U.S. We utilized a qualitative, human-centered design approach to look into the experiences of disabled workers who were asked to complete simulated digitized assessments. Participants indicated that assessments were (1) discriminatory and perpetuated biases throughout; (2) presented accessibility barriers; (3) caused emotionally taxing experiences; and (4) contributed to exclusion. The findings aim to inform employers, policymakers, advocates, and researchers and to suggest steps toward more effective and accessible digitized assessments.
Generative text-to-image (T2I) models often output images that have stereotypes of people with disabilities. One possibility to mitigate the risk of these biases is to intervene at the user level, supporting T2I users themselves in being able to identify biases and act accordingly. To understand how to design such support and its potential effectiveness, we implemented two interventions: (1) an education module to inform users of disability stereotypes in T2I images and (2) AI-generated feedback about potential stereotypes in a given image. We evaluated these options alone and in combination through a controlled experiment (N=103) and a qualitative study (N=10). Our results demonstrate that interface-based interventions can help users identify stereotypes, but that people do not always desire to avoid stereotypes. Participants wanted image subjects to "look" disabled, which sometimes inadvertently perpetuated stereotypes. Our results indicate clear ways for T2I interfaces to support users in prompting for and assessing images.
Interdependence has long been a core concept in Disability Studies and activism, offering a critical response to dominant ideals of independence. While Bennett et al.’s work introduced interdependence into accessibility research in HCI by linking it with research and design practices, the extent to which HCI has meaningfully engaged with the theoretical and political roots of the concept remains unclear. In this literature review, we systematically analyze 70 HCI accessibility papers that engage with the concept of interdependence. Guided by the PRISMA framework, we investigate how interdependence is conceptualized and applied in HCI, identifying strengths and shortcomings of current conceptualizations. Our findings reveal that interdependence is used across a range of use cases that broaden its scope, but that integration remains partial and fragmented, often disconnected from its origins in Disability Studies and activism. We conclude by calling for a more meaningful integration of interdependence into HCI accessibility research.
Vision-Language Models (VLMs) are increasingly used by blind and low-vision (BLV) people to identify and understand products in their everyday lives, such as food, personal care items, and household goods. Despite their prevalence, we lack an empirical understanding of how common image quality issues — such as blur, misframing, and rotation — affect the accuracy of VLM-generated captions and whether the resulting captions meet BLV people's information needs. Based on a survey of 86 BLV participants, we develop an annotated dataset of 1,859 product images from BLV people to systematically evaluate how image quality issues affect VLM-generated captions. While the best VLM achieves 98% accuracy on images with no quality issues, accuracy drops to 75% overall when quality issues are present, worsening considerably as issues compound. We discuss the need for model evaluations that center on disabled people's experiences throughout the process and offer concrete recommendations for HCI and ML researchers to make VLMs more reliable for BLV people.
As generative AI (GenAI) is integrated into everyday technologies, it offers new accessibility opportunities and risks for disabled people. However, little is known about how disabled people navigate GenAI in their everyday lives, particularly how trust, privacy, and intersectional identities affect these experiences. We present findings from seven cross-disability focus groups (N=20) that explore how disabled people navigate GenAI. Our findings reveal that while GenAI supports autonomy, efficiency, and communication, it also introduces accessibility taxes and ethical dilemmas. Although participants voiced skepticism, many continued using GenAI out of necessity. Finally, we found identity-based benefits and tensions, in which GenAI preserved and validated intersecting identities, but also misrepresented and erased those identities. We frame these negotiations as a constant balancing act between access and risk, urging research to further examine how ``access'' is conceptualized. We offer implications for creating GenAI tools that are transparent, trustworthy, and responsive to intersectional identities.
While accessibility (a11y) guidelines exist for 3D games and virtual worlds, their applicability to extended reality (XR)'s unique interaction paradigms (e.g., spatial tracking, kinesthetic interactions) remains unexplored. XR practitioners need practical guidance to successfully implement a11y guidelines under real-world constraints. We present the first evaluation of existing 3D a11y guidelines applied to XR development through semi-structured interviews with 25 XR practitioners across diverse organization contexts. We assessed 20 commonly-agreed a11y guidelines from six major resources across visual, motor, cognitive, speech, and hearing domains, comparing practitioners' development practices against guideline applicability to XR. Our investigation reveals that guidelines can be highly effective when designed as transformation catalysts rather than compliance checklists, but fundamental mismatches exist between existing 3D guidelines and XR requirements, creating both implementation barriers and design gaps. This work provides foundational insights towards developing a11y guidelines and support tools that address XR's distinct characteristics.
Social accessibility research faces a persistent tension: assistive technologies (AT) predominantly pursue independence, yet disabled people's experiences reveal rich preferences for interdependence. Our analysis of 90 papers from 2011-2025 uncovered that this stems from a deeper issue — which crystallized through dialogue with three bodies of theories: (1) self-determination theory (SDT), (2) symbolic interactionism, and (3) posthumanist perspectives and crip technoscience. SDT illuminates individual needs; symbolic interactionism addresses construction of social meaning and stigma; Posthumanist and crip technoscience together challenges normalcy, governance, and the human-machine boundary. Through their tensions, we identify relational sovereignty as an alternative telos — or goal — to autonomy. While our corpus equates autonomy with independence, sovereignty centers the power to choose between independence and interdependence. To operationalize this shift — from ``Can they do it?'' to ``Do they get to decide?'' — we introduce the Relational Sovereignty Matrix and four design interventions: (1) a sovereignty-centered reframing of SDT, (2) generative questions for justice-oriented reflection, (3) the idea of building through sovereign technical primitives, and (4) explicit consideration of power in AT design.