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.

Insights into the unique security and privacy practices, risks, and solutions for people with disabilities are currently fragmented across disciplines.In this work, we present a literature review of 33 papers published at leading human-computer interaction, accessibility, and usable security and privacy venues.We categorize the contributions of these papers-ranging from interventions to empirical studies of risks and behaviors-and identify key themes and implications.Papers in this corpus highlight 1) the opportunities and risks of the data collected by assistive technologies and security and privacy tools, 2) the inaccessibility or low usability of security and privacy solutions for people with disabilities, and 3) the utility of customized, contextual security and privacy solutions.We conclude with best practices for collecting data from disabled communities and implications for the design of assistive technologies and security/privacy tools.

Accessibility research has a broad mandate: use technology to make the world more accessible to disabled people. Yet, as a field, accessibility research lacks a clear characterization of what "accessibility" is. Furthermore, it has been historically limited in who is designed for, focusing on specific types of disability and often failing to consider how disability intersects with other identities. We set out to explicate what it means to make something accessible, grounded in the lived experiences of a diverse group of 25 disabled people. From our empirical findings, we develop a process for modeling accessibility. First, an individual assesses their experience of inaccess, specifically, the type of barrier they face, the technology repertoire they possess, and the contextual factors that shape how they address accessibility barriers. Then, having assessed an access barrier, they perform consequence calculus, weighing all available options to achieve access and deciding upon the option that best matches their priorities. We highlight the situated nature of access; people's identities, contextual factors, repertoires, and priorities all dictate their experience of accessibility.

Blind and low vision (BLV) individuals use Generative AI (GenAI) tools to interpret and manage visual content in their daily lives.While such tools can enhance the accessibility of visual content and enable greater user independence, they also introduce complex challenges around visual privacy.In this paper, we investigate the current practices and future design preferences of blind and low vision individuals through an interview study with 21 participants.Our findings reveal a range of current practices with GenAI that balance privacy, efficiency, and emotional agency, with users accounting for privacy risks across six key scenarios: selfpresentation, indoor spatial privacy, outdoor spatial privacy, social media sharing, sharing with employer or professional setup, and handling professional content as employers.Our findings reveal design preferences, including on-device processing, zero-retention guarantees, sensitive content redaction, privacy-aware appearance indicators, and multimodal tactile mirrored interaction methods.We conclude with actionable design recommendations to support user-centered visual privacy through GenAI, expanding the notion of privacy and responsible handling of others' information.

Slide deck accessibility is often studied for people who are blind or visually impaired, but rarely for other people with access needs. We first conducted focus groups with 17 people with slide deck access needs and found that their access needs differed greatly and often conflicted. Moreover, some people’s access needs changed throughout the day (e.g., needing lower contrast colors at night). Therefore, we conducted a design probe with 14 of the existing participants to understand the experience of using a plug-in that lets audience members at a presentation modify a local copy of the slides to meet their accessibility needs. We then interviewed four slide deck authors and presenters to offer a preview of the perspectives that other stakeholders of this tool might have. Finally, we created a functional prototype as a Google Slides plug-in with a subset of the features requested by the participants.

This paper reports on disability representation in images output from text-to-image (T2I) generative AI systems. Through eight focus groups with 25 people with disabilities, we found that models repeatedly presented reductive archetypes for different disabilities. Often these representations reflected broader societal stereotypes and biases, which our participants were concerned to see reproduced through T2I. Our participants discussed further challenges with using these models including the current reliance on prompt engineering to reach satisfactorily diverse results. Finally, they offered suggestions for how to improve disability representation with solutions like showing multiple, heterogeneous images for a single prompt and including the prompt with images generated. Our discussion reflects on tensions and tradeoffs we found among the diverse perspectives shared to inform future research on representation-oriented generative AI system evaluation metrics and development processes.

With the recent rapid rise in Generative Artificial Intelligence (GAI) tools, it is imperative that we understand their impact on people with disabilities, both positive and negative. However, although we know that AI in general poses both risks and opportunities for people with disabilities, little is known specifically about GAI in particular. To address this, we conducted a three-month autoethnography of our use of GAI to meet personal and professional needs as a team of researchers with and without disabilities. Our findings demonstrate a wide variety of potential accessibility-related uses for GAI while also highlighting concerns around verifiability, training data, ableism, and false promises.

People with disabilities face extra hardship in institutions of higher education because of accessibility barriers built into the educational system. While prior work investigates the needs of individual stakeholders, this work offers insights into the communication and collaboration between key stakeholders in creating access in institutions of higher education. The authors present reflections from their experiences working with disability service offices to meet their access needs and the results from interviewing 6 professors and 6 other disabled students about their experience in achieving access. Our results indicate that there are rich opportunities for technological solutions to support these stakeholders in communicating about and creating access.

The future of accessibility research is a topic we take up every day as researchers; yet it is important also to step back and ask ourselves about the most important, and overlooked, areas for inquiry in our field. With the rapid pace of change in both computational capabilities and the environmental, social, and political context in which disability plays out, we believe this is a critical time for such inquiry. This is even more important given the relatively narrow set of topics that accessibility research has focused on for most of our field’s history. We invite our community to come together to define what the next generation of accessibility research should engage with.

Accessibility research strives to develop technology that is useful for disabled people, but the research processes that we engage in do not always center disabled people in a way that allows us to shape artifacts so that they benefit disabled communities. In this workshop, we want to address core questions that are relevant in this context: How can research questions be defined in a way that shares power between research teams and technology users? How should research processes be designed to be broadly accessible for disabled people? And what are equitable ways of summarizing and sharing research findings in a way that allows disabled communities to critically appraise findings with us? Through discussion among all attendees, we want to develop a practical guide in disability-centered research that will be made available and further developed as a community resource when engaging in accessibility research.

For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. Creating flexible design tools with re-configurable optimizers that can tackle a variety of problems in a domain requires even more domain and programming expertise. We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools. OPTIMISM supports the implementation of metaheuristic optimization methods by factoring them into easy to implement and reuse components: objectives that measure desirable qualities in the domain, modifiers which make useful changes to designs, design and modifier selectors which determine how the optimizer steps through the search space, and stopping criteria that determine when to return results. Implementing optimizers with OPTIMISM shifts the burden of domain expertise from programmers to domain experts.

Digital avatars are an important part of identity representation, but there is little work on understanding how to represent disability. We interviewed 18 people with disabilities and related identities about their experiences and preferences in representing their identities with avatars. Participants generally preferred to represent their disability identity if the context felt safe and platforms supported their expression, as it was important for feeling authentically represented. They also utilized avatars in strategic ways: as a means to signal and disclose current abilities, access needs, and to raise awareness. Some participants even found avatars to be a more accessible way to communicate than alternatives. We discuss how avatars can support disability identity representation because of their easily customizable format that is not strictly tied to reality. We conclude with design recommendations for creating platforms that better support people in representing their disability and other minoritized identities.

The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally manufacturable personal protective equipment (PPE) . We evaluated the 623 submissions in this collection to understand: what makers contributed, how they were made, who made them, and key characteristics of their designs. We found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups. The NIH worked to review safe, effective designs but was overloaded by manufacturing-focused design adaptations. Our work contributes insights into: the outcomes of distributed, community-based medical making; the features that the community accepted as “safe” making; and how platforms can support regulated maker activities in high-risk domains.

Students with disabilities face numerous access barriers in higher education institutions. For example, many students struggle to receive the accommodations that they legally have a right to, and many course materials and tools are inaccessible (e.g., textbooks, required software, slide decks). Consequently, students with disabilities drop out of college at a higher rate than nondisabled students. In this dissertation, I aim to improve two core areas of inaccessibility for students with disabilities. First, I will learn about the common issues that arise when three stakeholders (disabled students, professors, and people working in disability service offices) work to fulfill technology-focused accommodations (e.g., slides, IDEs, lecture videos) for a student. Through this two part survey and interview/co-design study, I will develop design recommendations around how technology can better support this process. Second, I will apply techniques like optimization and natural language processing to build tools to identify and automatically repair common accessibility issues in a ubiquitous tool for teaching across departments: slide show presentations. By conducting this work, I will contribute software tools and design recommendations that will support disabled students in obtaining an accessible education.

Accessible design and technology could support the large and growing group of people with chronic illnesses. However, human computer interactions (HCI) has largely approached people with chronic illnesses through a lens of medical tracking or treatment rather than accessibility. We describe and demonstrate a framework for designing technology in ways that center the chronically ill experience. First, we identify guiding tenets: 1) treating chronically ill people not as patients but as people with access needs and expertise, 2) recognizing the way that variable ability shapes accessibility considerations, and 3) adopting a theoretical understanding of chronic illness that attends to the body. We then illustrate these tenets through autoethnographic case studies of two chronically ill authors using technology. Finally, we discuss implications for technology design, including designing for consequence-based accessibility, considering how to engage care communities, and how HCI research can engage chronically ill participants in research.

Tactile maps can help people who are blind or have low-vision navigate and familiarize themselves with unfamiliar locations. Ideally, tactile maps can be customized to an individual’s unique needs and abilities because of their limited space for representation. We present Maptimizer, a tool that generates tactile maps based on users’ preferences and requirements. Maptimizer uses a two stage optimization process to pair representations with geographic information and tune those representations to present that information more clearly. In a small user study, Maptimizer helped participants more successfully and efficiently identify locations of interest in unknown areas. These results demonstrate the utility of optimization techniques and generative design in complex accessibility domains.

Methods are fundamental to doing research and can directly impact who is included in scientific advances. Given accessibility research's increasing popularity and pervasive barriers to conducting and participating in research experienced by people with disabilities, it is critical to ask how methods are made accessible. Yet papers rarely describe their methods in detail. This paper reports on 17 interviews with accessibility experts about how they include both facilitators and participants with disabilities in popular user research methods. Our findings offer strategies for anticipating access needs while remaining flexible and responsive to unexpected access barriers. We emphasize the importance of considering accessibility at all stages of the research process, and contextualize access work in recent disability and accessibility literature. We explore how technology or processes could reflect a norm of accessibility. Finally, we discuss how various needs intersect and conflict and offer a practical structure for planning accessible research.

We present an ethnographic study of a maker community that conducted safety-driven medical making to deliver over 80,000 devices for use at medical facilities in response to the COVID-19 pandemic. To achieve this, the community had to balance their clinical value of safety with the maker value of broadened participation in design and production. We analyse their struggles and achievement through the artifacts they produced and the labors of key facilitators between diverse community members. Based on this analysis we provide insights into how medical maker communities, which are necessarily risk-averse and safety-oriented, can still support makers' grassroots efforts to care for their communities. Based on these findings, we recommend that design tools enable adaptation to a wider set of domains, rather than exclusively presenting information relevant to manufacturing. Further, we call for future work on the portability of designs across different types of printers which could enable broader participation in future maker efforts at this scale.

Alternative (alt) text provides access to descriptions of digital images for people who use screen readers. While prior work studied screen reader users’ (SRUs’) preferences about alt text and automatic alt text (i.e., alt text generated by artificial intelligence), little work examined the alt text author’s experience composing or editing these descriptions. We built two types of prototype interfaces for two tasks: authoring alt text and providing feedback on automatic alt text. Through combined interview-usability testing sessions with alt text authors and interviews with SRUs, we tested the effectiveness of our prototypes in the context of Microsoft PowerPoint. Our results suggest that authoring interfaces that support authors in choosing what to include in their descriptions result in higher quality alt text. The feedback interfaces highlighted considerable differences in the perceptions of authors and SRUs regarding “high-quality” alt text. Finally, authors crafted significantly lower quality alt text when starting from the automatic alt text compared to starting from a blank box. We discuss the implications of these results on applications that support alt text.

The COVID-19 pandemic forced many people to convert their daily work lives to a “virtual” format where everyone connected remotely from their home. In this new, virtual environment, accessibility barriers changed, in some respects for the better (e.g., more flexibility) and in other aspects, for the worse (e.g., problems including American Sign Language interpreters over video calls). Microsoft Research held its first cohort of all virtual interns in 2020. We the authors, full time and intern members and affiliates of the Ability Team, a research team focused on accessibility, reflect on our virtual work experiences as a team consisting of members with a variety of abilities, positions, and seniority during the summer intern season. Through our autoethnographic method, we provide a nuanced view into the experiences of a mixed-ability, virtual team, and how the virtual setting affected the team’s accessibility. We then reflect on these experiences, noting the successful strategies we used to promote access and the areas in which we could have further improved access. Finally, we present guidelines for future virtual mixed-ability teams looking to improve access.

Knitting is a popular craft that can be used to create customized fabric objects such as household items, clothing and toys. Additionally, many knitters find knitting to be a relaxing and calming exercise. Little is known about how disabled knitters use and benefit from knitting, and what accessibility solutions and challenges they create and encounter. We conducted interviews with 16 experienced, disabled knitters and analyzed 20 threads from six forums that discussed accessible knitting to identify how and why disabled knitters knit, and what accessibility concerns remain. We additionally conducted an iterative design case study developing knitting tools for a knitter who found existing solutions insufficient. Our innovations improved the range of stitches she could produce. We conclude by arguing for the importance of improving tools for both pattern generation and modification as well as adaptations or modifications to existing tools such as looms to make it easier to track progress

Medical making intersects opposing value systems of a medical “do no harm” ethos and makers’ drive to innovate. Since March 2020, online maker communities have formed to design, manufacture, and distribute personal protective equipment (PPE) and other medical devices needed to fight the COVID-19 pandemic. We present a participant observation study of 14 maker communities, which have developed differing driving principles for efforts with varied access to interdisciplinary expertise on online platforms that mutually shape collective action. Over time, these communities unintentionally align towards action-oriented or regulated practices because they often lack higher level insight and agency in choosing communication platforms. In response, we recommend: regulatory bodies to build coalitions with makers, online platforms to give communities more control over the presentation of information, and repositories to balance needs to distribute information while limiting the spread of misinformation.

Unprecedented maker efforts arose in response to COVID-19 medical supply gaps worldwide. Makers in the U.S., participated in peer-production activities to manufacture personal protective equipment (PPE). Whereas, medical makers, who innovate exclusively for points of care, pivoted towards safer, reliable PPE. What were their efforts to pivot medical maker infrastructure towards reliable production of safe equipment at higher volumes? We interviewed 13 medical makers as links between institutions, maker communities, and wider regional industry networks. These medical makers organized stopgap manufacturing in institutional spaces to resolve acute shortages (March–May) and chronic shortages (May–July). They act as intermediaries in efforts to prototype and produce devices under regulatory, material, and human constraints of a pandemic. We re-frame their making efforts as repair work to offer an alternate critical view of optimism around making for crisis. We contribute an understanding of these efforts to inform infrastructure design for making with purpose and safety leading to opportunities for community production of safe devices at scale.

Accessibility research has grown substantially in the past few decades, yet there has been no literature review of the field. To understand current and historical trends, we created and analyzed a dataset of accessibility papers appearing at CHI and ASSETS since ASSETS' founding in 1994. We qualitatively coded areas of focus and methodological decisions for the past 10 years (2010-2019, N=506 papers), and analyzed paper counts and keywords over the full 26 years (N=836 papers). Our findings highlight areas that have received disproportionate attention and those that are underserved--for example, over 43% of papers in the past 10 years are on accessibility for blind and low vision people. We also capture common study characteristics, such as the roles of disabled and nondisabled participants as well as sample sizes (e.g., a median of 13 for participant groups with disabilities and older adults). We close by critically reflecting on gaps in the literature and offering guidance for future work in the field.

We introduce HomeSound, an in-home sound awareness system for Deaf and hard of hearing (DHH) users. Similar to the Echo Show or Nest Hub, HomeSound consists of a microphone and display, and uses multiple devices installed in each home. We iteratively developed two prototypes, both of which sense and visualize sound information in real-time. Prototype 1 provided a floorplan view of sound occurrences with waveform histories depicting loudness and pitch. A three-week deployment in four DHH homes showed an increase in participants' home- and self-awareness but also uncovered challenges due to lack of line of sight and sound classification. For Prototype 2, we added automatic sound classification and smartwatch support for wearable alerts. A second field deployment in four homes showed further increases in awareness but misclassifications and constant watch vibrations were not well received. We discuss findings related to awareness, privacy, and display placement and implications for future home sound awareness technology.

Spreadsheet systems are used for storing and analyzing data across domains by programmers and non-programmers alike.While spreadsheet systems have continued to support increasingly large datasets, they are prone to hanging and freezing while performing computations even on much smaller ones. We present a benchmarking study that evaluates and compares the performance of three popular systems, Microsoft Excel, LibreOffice Calc, and Google Sheets, on a range of canonical spreadsheet computation operations. We find that spreadsheet systems lack interactivity for several operations, on datasets well below their advertised scalability limits. We further evaluate whether spreadsheet systems adopt database optimization techniques such as indexing, intelligent data layout, and incremental and shared computation,to efficiently execute computation operations. We outline several ways future spreadsheet systems can be redesigned to offer interactive response times on large datasets.

Social media platforms support the sharing of written text, video, and audio. All of these formats may be inaccessible to people who are deaf or hard of hearing (DHH), particularly those who primarily communicate via sign language, people who we call Deaf signers. We study how Deaf signers engage with social platforms, focusing on how they share content and the barriers they face. We employ a mixed-methods approach involving seven in-depth interviews and a survey of a larger population (n = 60). We find that Deaf signers share the most in written English, despite their desire to share in sign language. We further identify key areas of difficulty in consuming content (e.g., lack of captions for spoken content in videos) and producing content (e.g., captioning signed videos, signing into a phone camera) on social media platforms. Our results both provide novel insights into social media use by Deaf signers and reinforce prior findings on DHH communication more generally, while revealing potential ways to make social media platforms more accessible to Deaf signers.

Spreadsheet tools are ubiquitous for interactive adhoc data management and analysis. With increasing dataset sizes, spreadsheet tools fall short-they freeze during heavy computation within the sheet (interactivity); they are hard to navigate when datasets go beyond a certain size (navigability); they only support cell-at-a-time computation, severely limiting analysis capabilities (expressiveness). We have been developing DATASPREAD to holistically unify databases and spreadsheets to leverage the benefits of both, with a spreadsheet-like front-end and a database-like backend. We demonstrate three key features of DATASPREAD to address the aforementioned spreadsheet scalability challenges in interactivity, navigability, and expressiveness1. Our demonstration will let attendees perform typical analysis tasks on Microsoft Excel and DATASPREAD side-by-side, providing a clear understanding of the improvements offered by DATASPREAD over traditional spreadsheet tools.

Spreadsheet systems enable users to store and analyze data in an intuitive and flexible interface. Yet the scale of data being analyzed often leads to spreadsheets hanging and freezing on small changes. We propose a new asynchronous formula computation framework: instead of freezing the interface we return control to users quickly to ensure interactivity, while computing the formulae in the background. To ensure consistency, we indicate formulae being computed in the background via visual cues on the spreadsheet. Our asynchronous computation framework introduces two novel challenges: (a) How do we identify dependencies for a given change in a bounded time? (b) How do we schedule computation to maximize the number of spreadsheet cells available to the user over time? We bound the dependency identification time by compressing the formula dependency graph lossily, a problem we show to be NP-Hard. A compressed dependency table enables us to quickly identify the spreadsheet cells that need recomputation and indicate them as such to users. Finding an optimal computation schedule to maximize cell availability is also NP-Hard, and even merely obtaining a schedule can be expensive-we propose an on-the-fly scheduling technique to address this. We have incorporated asynchronous computation in DataSpread, a scalable spreadsheet system targeted at operating on arbitrarily large datasets on a spreadsheet frontend.