Sign language processing holds great promise for advancing societal inclusivity, yet it often excludes meaningful participation from the Deaf community, raising ethical and practical concerns about the applicability of AI solutions to their needs. This paper addresses these gaps through two interrelated studies. First, surveys identify differences in priorities and expectations between machine learning (ML) practitioners and Deaf American Sign Language (ASL) signers. Second, paired co-design sessions bring ML and ASL experts together to generate guiding questions that support practices for aligning AI development with community goals. Our findings reveal critical points of friction that reflect deeper systemic and epistemic barriers to effective collaboration. By synthesizing unique and shared insights from both groups, we provide empirically grounded resources to guide collaborative frameworks that promote the agency and expertise of the Deaf community. This research paves actionable pathways toward equitable, community-centered advancements in AI.

Autonomous navigation robots can increase the independence of blind people but often limit user control-following what is called in Japanese an "omakase" approach where decisions are left to the robot. This research investigates ways to enhance user control in social robot navigation, based on two studies conducted with blind participants. The first study, involving structured interviews (N=14), identified crowded spaces as key areas with significant social challenges. The second study (N=13) explored navigation tasks with an autonomous robot in these environments and identified design strategies across different modes of autonomy. Participants preferred an active role, termed the "boss" mode, where they managed crowd interactions, while the "monitor" mode helped them assess the environment, negotiate movements, and interact with the robot. These findings highlight the importance of shared control and user involvement for blind users, offering valuable insights for designing future social navigation robots.

Blind people are often called to contribute image data to datasets for AI innovation with the hope for future accessibility and inclusion. Yet, the visual inspection of the contributed images is inaccessible. To this day, we lack mechanisms for data inspection and control that are accessible to the blind community. To address this gap, we engage 10 blind participants in a scenario where they wear smartglasses and collect image data using an AI-infused application in their homes. We also engineer a design probe, a novel data access interface called AccessShare, and conduct a co-design study to discuss participants' needs, preferences, and ideas on consent, data inspection, and control. Our findings reveal the impact of interactive informed consent and the complementary role of data inspection systems such as AccessShare in facilitating communication between data stewards and blind data contributors. We discuss how key insights can guide future informed consent and data control to promote inclusive and responsible data practices in AI.

To ensure that AI-infused systems work for disabled people, we need to bring accessibility datasets sourced from this community in the development lifecycle. However, there are many ethical and privacy concerns limiting greater data inclusion, making such datasets not readily available. We present a pair of studies where 13 blind participants engage in data capturing activities and reflect with and without probing on various factors that influence their decision to share their data via an AI dataset. We see how different factors influence blind participants' willingness to share study data as they assess risk-benefit tradeoffs. The majority support sharing of their data to improve technology but also express concerns over commercial use, associated metadata, and the lack of transparency about the impact of their data. These insights have implications for the development of responsible practices for stewarding accessibility datasets, and can contribute to broader discussions in this area.

As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this paper, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets–datasets sourced from people with disabilities and older adults—that can potentially play an important role in mitigating bias for inclusive AI-infused applications. We examine the current state of representation within datasets sourced by people with disabilities by reviewing publicly-available information of 190 datasets, we call these accessibility datasets. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. Additionally, we investigate how the sensitive and complex nature of demographic variables makes classification difficult and inconsistent (e.g., gender, race & ethnicity), with the source of labeling often unknown. By reflecting on the current challenges and opportunities for representation of disabled data contributors, we hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.

Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.

People with visual impairments often require mobility assistance of sighted guides but they are not always available. Recent technological strides have opened up new directions for sighted guidance services, assigning guides from a network of remote workers to provide real-time assistance via audio/video communication. However, little has been known regarding desirable support characteristics of remote guides or challenges experienced in guide practices without the requisite expertise. To recommend support strategies that contribute to facilitating a successful platform for remote sighted guidance, this paper presents a comparative study of the performance of trained and untrained sighted guides who are recruited for a remote scenario in assisting people with visual impairments in indoor navigation. As an outcome of this research, we provide a deeper understanding of design opportunities for HCI to scaffold requirements of remote guides, such that their collaborative efforts and environmental knowledge influence the user experience. Based on our empirical insights, we suggest to develop the expertise of remote guides through: a) preliminary guidance cooperation awareness b) guidelines for verbal description methods, and c) approaches to compensate for the lack of environmental knowledge.

Research in group activity analysis has put attention to monitor the work and evaluate group and individual performance, which can be reflected towards potential improvements in future group interactions. As a new means to examine individual or joint actions in the group activity, our work investigates the potential of detecting and disambiguating hands of each person in first-person points-of-view videos. Based on the recent developments in automated hand-region extraction from videos, we develop a new multiple-egocentric-video browsing interface that gives easy access to the frames of 1) individual action when only the hands of the viewer are detected, 2) joint action when collective hands are detected, and 3) the viewer checking the others' action as only their hands are detected. We take the evaluation process to explore the effectiveness of our interface with proposed hand-related features which can help perceive actions of interests in the complex analysis of videos involving co-occurred behaviors of multiple people.

This paper presents a novel interface to support video coding of social attention in the assessment of children with autism spectrum disorder. Video-based evaluations of social attention during therapeutic activities allow observers to find target behaviors while handling the ambiguity of attention. Despite the recent advances in computer vision-based gaze estimation methods, fully automatic recognition of social attention under diverse environments is still challenging. The goal of this work is to investigate an approach that uses automatic video analysis in a supportive manner for guiding human judgment. The proposed interface displays visualization of gaze estimation results on videos and provides GUI support to allow users to facilitate agreement between observers by defining social attention labels on the video timeline. Through user studies and expert reviews, we show how the interface helps observers perform video coding of social attention and how human judgment compensates for technical limitations of the automatic gaze analysis.

This work presents a novel user interface applying 3D visualization to understand complex group activities from multiple first-person videos. The proposed interface is designed to assist video viewers to easily understand the collaborative relationships of group activity based on where the individual worker is located in a workspace and how multiple workers are positioned to one another during the group activity. More specifically, the interface not only shows all recorded first-person videos but also visualizes the 3D position and orientation of each view point (i.e., the 3D position of each worker wearing a head-mounted camera) with a reconstructed 3D model of the workspace. Our user study confirms that the 3D visualization helps video viewers to understand geometric information of a worker and collaborative relationships of group activity easily and accurately.

Active involvement of users with disabilities is difficult to employ during the iterative stages of the design process due to high costs and effort associated with user studies. This research proposes a user centered design (UCD) strategy to incorporate the use of gaze-contingent tunnel vision simulation with sighted individuals to facilitate rapid prototyping of accessible interfaces. Through three types of validation studies, we examined how our simulation techniques can provide the opportunity for continued evaluation and refinement of the design. Our simulation approach was effective in emulating scanning behaviors caused by tunnel vision along with grasping user feedback to recognize user interface and usability criteria early in the design cycle.