Introduction Digital accessibility involves designing digital systems and services to enable
access for individuals, including those with disabilities, including visual, auditory, motor, or …

While various frameworks and heuristics exist within the HCI community to guide research
and design for vulnerable populations, most are centered on the researcher's involvement …

Large language models (LLMs) trained on real-world data can inadvertently reflect harmful
societal biases, particularly toward historically marginalized communities. While previous …

Disaggregated evaluations of AI systems, in which system performance is assessed and
reported separately for different groups of people, are conceptually simple. However, their …

Existing assistive technologies (AT) often fail to support the unique needs of blind and
visually impaired (BVI) people. Thus, BVI people have become domain experts in …

As new forms of data capture emerge to power new AI applications, questions abound about
the ethical implications of these data collection practices. In this paper, we present clinicians' …

The applications of automatic speech recognition (ASR) systems are proliferating, in part
due to recent significant quality improvements. However, as recent work indicates, even …

Growing awareness of the capacity of AI to inflict harm has inspired efforts to delineate
principles for 'trustworthy AI'and, from these, objective indicators of 'trustworthiness' for …

People with complex communication needs (CCNs) can use high-tech augmentative and
alternative communication (AAC) devices and systems to compensate for communication …

Given the scale at which online harassment occurs, researchers and practitioners alike have
turned to computationally driven approaches to address it. However, because harassment is …