Alt text is a core accessibility feature for blind and low vision (BLV) users, yet prevailing conventions emphasize objective description. While effective for many functional images, these norms can underserve paintings, where meaning emerges through formal attributes and interpretation. We qualitatively analyze alt text for 91 paintings from an open-access collection and find heavy reliance on templated descriptions that are often redundant and provide uneven support for interpretive engagement. We contribute a provisional taxonomy that links objective formal cues to plausible interpretive attributes that distinguishes observation from interpretation. We position this taxonomy as a scaffold for supporting accountable interpretive access in painting alt text.

Public art can hold cultural, social, political, and aesthetic significance, enriching urban environments and promoting well-being. However, a majority of urban art is inaccessible to blind and low vision (BLV) people. Most art access research has focused on private and curated settings (e.g., museums, galleries) and most urban access work has centered on outdoor navigation, leaving urban and public art accessibility largely understudied. We conducted semi-structured interviews with 16 BLV participants, using design probes featuring AI-generated descriptions and real-time AI interactions to investigate preferences for both discovering and engaging with urban art. We found that BLV people valued spontaneous art exploration, multisensory (e.g., tactile, auditory, olfactory) engagement, and detailed descriptions of culturally significant artwork. Participants also highlighted challenges distinct to urban art contexts: safety took precedence over art exploration, multisensory access measures could be disruptive to others in the public space, and inaccurate AI descriptions could lead to cultural erasure. Our contributions include empirical insights on BLV preferences for urban art discovery and engagement, seven design dimensions for public art access solutions, and implications for expanding HCI urban accessibility research beyond navigation.

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.

Visual media is often made accessible to blind and low vision (BLV) people through audio description (AD), typically written by experts. Prior efforts to increase the scale of description output have involved sighted novices as describers or used generative AI (GenAI) to automatically convert images to text; however, description quality remains a concern. To support novice describers in writing high quality descriptions, we designed and developed a GenAI-powered online tool, "Guidedogs," featuring five dogs with unique names, images, and voices that provided immediate and varied feedback on draft descriptions. We piloted the tool during a large hackathon-style description workshop in 2024. Through 17 semi-structured interviews, we explored the efficacy of using metaphors as personas for AI assistants and gathered insights on participants' perceptions on using AI for accessibility purposes. We contribute preliminary insights on generative AI assistant personas in an accessibility context and share design considerations to guide future work.

Despite the growth of video as a medium, videos remain inaccessible to many people. Prior video accessibility research has focused primarily on blind and low vision or d/Deaf and hard of hearing audiences. However, the video watching experiences of people with ADHD are largely unexplored. Through semi-structured interviews with 20 participants self-identifying with ADHD, we uncovered video watching frustrations, current strategies for access, and desired accessibility features. Participants faced both overstimulation and understimulation from visuals and audio (e.g., flashing lights, slower speech), which impacted their attention, engagement, and information retention. Common strategies included altering video speed, using captions, and leveraging timestamps for skipping through videos. Participants desired adjustable sound channels for aiding focus, video summaries for retaining information, and warnings for preempting sensory discomfort. We close by discussing (1) design recommendations for platforms and creators to support users in achieving their viewing goals and (2) ADHD-inclusive design principles.

Many blind and low vision (BLV) people are excluded from professional roles that may involve visual tasks due to access barriers and persisting stigmas. Advancing generative AI systems can support BLV people through providing contextual and personalized visual descriptions for creation, critique, and consumption. In this workshop paper, we provide design suggestions for how visual descriptions can be better contextualized for multiple professional tasks. We conclude by discussing how these designs can improve autonomy, inclusion, and skill development over time.

Disabled people on social media often experience ableist hate and microaggressions. Prior work has shown that platform moderation often fails to remove ableist hate leaving disabled users exposed to harmful content. This paper examines how personalized moderation can safeguard users from viewing ableist comments. During interviews and focus groups with 23 disabled social media users, we presented design probes to elicit perceptions on configuring their filters of ableist speech (e.g. intensity of ableism and types of ableism) and customizing the presentation of the ableist speech to mitigate the harm (e.g. AI rephrasing the comment and content warnings). We found that participants preferred configuring their filters through types of ableist speech and favored content warnings. We surface participants distrust in AI-based moderation, skepticism in AI's accuracy, and varied tolerances in viewing ableist hate. Finally we share design recommendations to support users' agency, mitigate harm from hate, and promote safety.

With the increasing adoption of social virtual reality (VR), it is critical to design inclusive avatars. While researchers have investigated how and why blind and d/Deaf people wish to disclose their disabilities in VR, little is known about the preferences of many others with invisible disabilities (e.g., ADHD, dyslexia, chronic conditions). We filled this gap by interviewing 15 participants, each with one to three invisible disabilities, who represented 22 different invisible disabilities in total. We found that invisibly disabled people approached avatar-based disclosure through contextualized considerations informed by their prior experiences. For example, some wished to use VR’s embodied affordances, such as facial expressions and body language, to dynamically represent their energy level or willingness to engage with others, while others preferred not to disclose their disability identity in any context. We define a binary framework for embodied invisible disability expression (public and private) and discuss three disclosure patterns (Activists, Non-Disclosers, and Situational Disclosers) to inform the design of future inclusive VR experiences.

Public artwork, from vibrant wall murals to captivating sculptures, can enhance the aesthetic of urban spaces, foster a sense of community and cultural identity, and help attract visitors. Despite its benefits, most public art is visual, making it often inaccessible to blind and low vision (BLV) people. In this workshop paper, we first draw on art literature to help define the space of public art, identify key differences with curated art shown in museums or galleries, and discuss implications for accessibility. We then enumerate how existing art accessibility techniques may (or may not) transfer to urban art spaces. We close by presenting future research directions and reflecting on the growing role of AI in making art accessible.

Content creators (e.g., gamers, activists, vloggers) with marginalized identities are at-risk of experiencing hate and harassment. This paper examines the ableist hate and harassment that disabled content creators experience on social media. Through surveys (N=50) and interviews (N=20) with disabled creators, we developed a taxonomy of 11 types of ableist hate and harassment (e.g., eugenics-related speech, denial and stigmatization of accessibility) and outlined how ableism harms creators’ well-being and content creation practices. Using statistical modeling, we investigated differences in ableist experiences given creators’ intersecting identities such as race and sexuality. We found that LGBTQ disabled creators face significantly more ableist hate compared to non-LGBTQ disabled creators. Lastly, we discuss our findings through an infrastructure lens to highlight how disabled creators experience platform-enabled ableism, undergo labor to cope with hate, and develop strategies to safeguard against future hate.

Activism can take a multitude of forms, including protests, social media campaigns, and even public art. The uniqueness of public art lies in that both the act of creation and the artifacts created can serve as activism. Furthermore, public art is often site-specific and can be created with (e.g., commissioned murals) or without permission (e.g., graffiti art) of the site's owner. However, the majority of public art is inaccessible to blind and low vision people, excluding them from political and social action. In this position paper, we build on a prior crowdsourced mural description project and describe the design of one potential process artifact, ARtivism, for making public art more accessible via augmented reality. We then discuss tensions that may occur at the intersection of public art, activism, and technology.

While audio description (AD) is the standard approach for making videos accessible to blind and low vision (BLV) people, existing AD guidelines do not consider BLV users' varied preferences across viewing scenarios. These scenarios range from how-to videos on YouTube, where users seek to learn new skills, to historical dramas on Netflix, where a user's goal is entertainment. Additionally, the increase in video watching on mobile devices provides an opportunity to integrate nonverbal output modalities (e.g., audio cues, tactile elements, and visual enhancements). Through a formative survey and 15 semi-structured interviews, we identified BLV people's video accessibility preferences across diverse scenarios. For example, participants valued action and equipment details for how-to videos, tactile graphics for learning scenarios, and 3D models for fantastical content. We define a six-dimensional video accessibility design space to guide future innovation and discuss how to move from "one-size-fits-all" paradigms to scenario-specific approaches.

Social virtual reality (VR) has become one of the most popular forms of VR. However, despite years of research on how VR interventions can be useful as diagnostic or therapeutic tools for neurodivergent (ND) users, there has been little examination of how accessible social VR may be for such ND individuals. In this paper, we describe an ongoing user study with participants who self-identify with both autism and ADHD (AuDHD) and also self-identify with facing frequent challenges with social interaction. So far, we have recruited four AuDHD participants; we had each participant briefy explore a world on a popular commercial social VR platform and then reflect on this experience afterward in a longer interview section. Through this process, we uncovered various accessibility challenges in social VR, such as difficulties with navigating social norms or managing certain sensory inputs. We also noted ideas on potential accommodations, like a text-based prompt system that can suggest "appropriate" conversation responses. Our work outlines opportunities to improve the accessibility of social VR for an often-overlooked user group.

As social virtual reality (VR) experiences become more popular, it is critical to design accessible and inclusive embodied avatars. At present, there are few, if any, customization features for invisible disabilities (e.g., chronic health conditions, mental health conditions, neurodivergence) in social VR platforms. To our knowledge, researchers have yet to explore how people with invisible disabilities want to self-represent and disclose disabilities through social VR avatars. We fill this gap in current accessibility research by centering the experiences and preferences of people with invisible disabilities. We conducted semi-structured interviews with nine participants and found that people with invisible disabilities used a unique, indirect approach to inform dynamic disclosure practices. Participants were interested in toggling representation on/off across contexts and shared ideas for representation through avatar design. In addition, they proposed ways to make the customization process more accessible (e.g., making it easier to import custom designs). We see our work as a vital contribution to the growing literature that calls for more inclusive social VR.

With improvements in automated speech recognition and increased use of videoconferencing, real-time captioning has changed significantly. This shift toward broadly available but less accurate captioning invites exploration of the role hearing conversation partners play in shaping the accessibility of a conversation to d/Deaf and hard of hearing (DHH) captioning users. While recent work has explored DHH individuals' videoconferencing experiences with captioning, we focus on established groups' current practices and priorities for future tools to support more accessible online conversations. Our study consists of three codesign sessions, conducted with four groups (17 participants total, 10 DHH, 7 hearing). We found that established groups crafted social accessibility norms that met their relational contexts. We also identify promising directions for future captioning design, including the need to standardize speaker identification and customization, opportunities to provide behavioral feedback during a conversation, and ways that videoconferencing platforms could enable groups to set and share norms.

While audio description (AD) is a standard method for making traditional videos more accessible to blind and low vision (BLV) users, we lack an understanding of how to make 360° videos accessible while preserving their immersive nature. Through individual interviews and collaborative design workshops, we explored ways to improve 360° video accessibility with immersion and engagement in mind. Our design workshops presented a unique opportunity for participants with diverse backgrounds to build on each others’ personal and professional experiences and collaboratively develop accessible 360° video prototypes. Participants included both AD creators and users, with a focus on BLV AD creators as their perspectives are underrepresented in prior work. We found that immersive video accessibility went beyond an extension of traditional video accessibility techniques. Participants valued accurate vocabulary and different points of view for descriptions, preferred a variety of presentation locations for spatialized AD, appreciated sound effects for setting the mood and subtly guiding, and wished to engage multiple senses to boost engagement. We conclude with implications for immersive media accessibility and future research directions to support disabled people as creators of access technology.

Auditory interfaces increasingly support access to website content, through recent advances in voice interaction. Typically, however, these interfaces provide only limited audio styling, collapsing rich visual design into a static audio output style with a single synthesized voice. To explore the potential for more aesthetic and intuitive sound design for websites, we prompted 14 professional sound designers to create auditory website mockups and interviewed them about their designs and rationale. Our findings reveal their prioritized design considerations (aesthetics and emotion, user engagement, audio clarity, information dynamics, and interactivity), specific sound design ideas to support each consideration (e.g., replacing spoken labels with short, memorable audio expressions), and challenges with applying sound design practices to auditory websites. These findings provide promising direction for how to support designers in creating richer auditory website experiences.

Audio description (AD), an additional narration track that conveys visual information in media, improves video accessibility for blind or low vision (BLV) viewers. Despite being the primary beneficiaries of AD, BLV audiences are limited in how they can contribute to the AD writing process due to technology inaccessibility and societal biases. In this poster, we (1) prototype and test AccessibleAD, an accessible AD writing system, (2) analyze what context and features are valued by BLV description writers, and (3) explore nonvisual involvement in AD creation. This work expands on existing literature regarding audio description and explores best practices for expanding access to AD writing.

With advances in expressive speech synthesis and conversational understanding, an ever-increasing amount of digital content---including social and personal content---can be consumed through voice. Voice has long been known to convey personal characteristics and emotional states, both of which are prominent aspects of social media. Yet, no study has investigated voice design requirements for social media platforms. We interviewed 15 active social media users about their preferences on using synthesized voices to represent their profiles. Our findings show that participants want to have control over how a voice delivers their content, such as the personality and emotion with which the voice speaks, because these prosodic variations can impact users' online personas and interfere with impression management. We report motivations behind customizing or not customizing voice characteristics in different scenarios, and uncover key challenges around usability and the potential for stereotyping. We argue that synthesized speech for social media should be evaluated not only on listening experience and voice quality but also on its expressivity, degree of customizability, and ability to adapt to contexts (e.g., social media platforms, groups, individual posts). We discuss how our contribution confirms and extends knowledge of voice technology design and online self-presentation, and offer design considerations for voice personalization related to social interactions.

With advances in expressive speech synthesis and conversational understanding, an ever-increasing amount of digital content---including social and personal content---can be consumed through voice. Voice has long been known to convey personal characteristics and emotional states, both of which are prominent aspects of social media. Yet, no study has investigated voice design requirements for social media platforms. We interviewed 15 active social media users about their preferences on using synthesized voices to represent their profiles. Our findings show that participants want to have control over how a voice delivers their content, such as the personality and emotion with which the voice speaks, because these prosodic variations can impact users' online personas and interfere with impression management. We report motivations behind customizing or not customizing voice characteristics in different scenarios, and uncover key challenges around usability and the potential for stereotyping. We argue that synthesized speech for social media should be evaluated not only on listening experience and voice quality but also on its expressivity, degree of customizability, and ability to adapt to contexts (e.g., social media platforms, groups, individual posts). We discuss how our contribution confirms and extends knowledge of voice technology design and online self-presentation, and offer design considerations for voice personalization related to social interactions.