Mahadeo A. Sukhai

Zahra Ahmadi, Peter R. Lewis, Mahadeo A. Sukhai

Artificial Intelligence (AI) is increasingly employed to enhance assistive technologies, yet it can fail in various ways. We conducted a systematic literature review of research into AI-based assistive technology for persons with visual impairments. Our study shows that most proposed technologies with a testable prototype have not been evaluated in a human study with members of the sight-loss community. Furthermore, many studies did not consider or report failure cases or possible risks. These findings highlight the importance of inclusive system evaluations and the necessity of standardizing methods for presenting and analyzing failure cases and threats when developing AI-based assistive technologies.

Yichun Zhao, Miguel A. Nacenta, Mahadeo A. Sukhai et al.

Despite recognition of the value of diversity, the way work takes place can fail to support blind or low-vision employees, especially in collaborative work settings. This paper examines how professional teams with diverse visual abilities use information representations (e.g., PDF documents, spreadsheets and charts). A diary study with follow-up individual interviews (23 participants with mixed abilities from 5 teams) and 2 separate focus groups (7 participants from 2 other teams) allowed us to characterize key dimensions of the role of representations in the workplace into four types of interrelated failures and workarounds, influenced by workplace stigmas and shaped by evolving social dynamics towards interdependent information work. We contribute this new empirically supported conceptual understanding of representation use in workplaces that can help design and improve the experiences of mixed-ability teams doing knowledge work in the current technological landscape.

Chukwunonso Henry Nwokoye, Maria J. P. Peixoto, Akriti Pandey et al.

The increasing integration of Artificial Intelligence (AI) into everyday life makes it essential to explain AI-based decision-making in a way that is understandable to all users, including those with disabilities. Accessible explanations are crucial as accessibility in technology promotes digital inclusion and allows everyone, regardless of their physical, sensory, or cognitive abilities, to use these technologies effectively. This paper presents a systematic literature review of the research on the accessibility of Explainable Artificial Intelligence (XAI), specifically considering persons with sight loss. Our methodology includes searching several academic databases with search terms to capture intersections between XAI and accessibility. The results of this survey highlight the lack of research on Accessible XAI (AXAI) and stress the importance of including the disability community in XAI development to promote digital inclusion and accessibility and remove barriers. Most XAI techniques rely on visual explanations, such as heatmaps or graphs, which are not accessible to persons who are blind or have low vision. Therefore, it is necessary to develop explanation methods through non-visual modalities, such as auditory and tactile feedback, visual modalities accessible to persons with low vision, and personalized solutions that meet the needs of individuals, including those with multiple disabilities. We further emphasize the importance of integrating universal design principles into AI development practices to ensure that AI technologies are usable by everyone.