This paper documents the process undertaken by StammerTalk , a grassroots community of Chinese-speaking people who stutter, to autonomously collect and curate stuttered speech data for more inclusive speech AI models. While people with disabilities are often excluded or treated merely as the subjects of AI data collection, our work introduces a new model for disability data collection in which the disability community exerts agency and control over their personal data and data-driven experiences. Our ethnographic data show that community-led data collection not only produces data needed to represent the community in AI systems, but also empowers the community and its members, by embracing - rather than concealing - stuttering and stutterer identity, and strengthening the social bonds of the community. Recognizing the lack of adequate socio-technical infrastructure for community-led, grassroots data collection, we discuss practical challenges, as well as the strategies and factors for communities to succeed in similar endeavors.

The video game Animal Crossing: New Horizons (AC:NH) launched during the COVID-19 pandemic and families turned to it as a game to play together during isolation. This interview study of 27 families considered how families used AC:NH for Joint Media Engagement (JME), where family members engage with media content together, interacting with each other and bringing additional meaning to the experience. We find that the design of AC:NH well facilitates Takeuchi and Stevens's six conditions for productive JME. Furthermore, we identify and discuss additional conditions that contribute to productive JME: variety and flexibility in play styles that amplify mutual engagement, support for disentrained play that enables new forms of "joint" engagement, and scaffolding for affective interactions. This is followed by an exploration of how the COVID-19 pandemic affected JME. We conclude with design implications for building games to support productive JME for families through design for persistent shared spaces, flexible in-game progress, and social life simulation.

Respectfully and adequately referring to people with various disabilities is difficult due to societal norms and constantly evolving languages. In this work, we address the question of how expert researchers in the field of accessibility are referring to people with disabilities and whether this terminology corresponds to how people with disabilities prefer to be addressed. By conducting a systematic literature review of the past three ASSETS proceeding, we summarize how accessibility researchers are currently referring to people with disabilities in English. A survey of 63 people with disabilities further revealed that while researchers from ASSETS are using terms that are mostly aligned with participants’ expectations, the same terminologies can be perceived both respectful and disrespectful by varying participants. Through this preliminary work, we pave the path for researchers to further explore respectful terminology and encourage researchers to improve the inclusivity and diversity of language use in our community.

Roughly 1 in 3 people around the world are affected by cognitive or mental disabilities at some point in their lives, yet people often face a variety of barriers when seeking support and receiving diagnosis from healthcare professionals. While prior work found that people with such disabilities assess themselves using online tests and assessments, it remains unknown whether and how effectively these tests fill gaps in healthcare and general support systems. To find out, we interviewed 17 adults with cognitive or mental disabilities about their motivation for and experience using online tests. We learned that online tests act as an important resource that address the shortcomings in support systems for people with professionally diagnosed or suspected cognitive or mental disabilities. In particular, online tests can lower barriers to a professional diagnosis, provide valuable information about the nuances of a disability, and support people in forming a disability identity – an invaluable step towards a positive acceptance of oneself. Our results also uncovered challenges and risks that prevent people with known or suspected health conditions from fully taking advantage of online tests. Based on these findings, we discuss how online tests can be better leveraged to support people with cognitive or mental disabilities before and after professional diagnosis.

Keyboard-based emoji entry can be challenging for people with visual impairments: users have to sequentially navigate emoji lists using screen readers to find their desired emojis, which is a slow and tedious process. In this work, we explore the design and benefits of emoji entry with speech input, a popular text entry method among people with visual impairments. After conducting interviews to understand blind or low vision (BLV) users’ current emoji input experiences, we developed Voicemoji, which (1) outputs relevant emojis in response to voice commands, and (2) provides context-sensitive emoji suggestions through speech output. We also conducted a multi-stage evaluation study with six BLV participants from the United States and six BLV participants from China, finding that Voicemoji significantly reduced entry time by 91.2% and was preferred by all participants over the Apple iOS keyboard. Based on our findings, we present Voicemoji as a feasible solution for voice-based emoji entry.

As reading increasingly shifts from paper to online media, many web browsers now provide a "Reader View,'' which modifies web page layout and design for better readability. However, research has yet to establish whether Reader Views are effective in improving readability and how they might change the user experience. We characterize how Mozilla Firefox's Reader View significantly reduces the visual complexity of websites by excluding menus, images, and content. We then conducted an online study with 391 participants (including 42 who self-reported having been diagnosed with dyslexia), showing that compared to standard websites the Reader View increased reading speed by 5% for readers on average, and significantly improved perceived readability and visual appeal. We suggest guidelines for the design of websites and browsers that better support people with varying reading skills.

Abstract Latent spaces—reduced‐dimensionality vector space embeddings of data, fit via machine learning—have been shown to capture interesting semantic properties and support data analysis and synthesis within a domain. Interpretation of latent spaces is challenging because prior knowledge, sometimes subtle and implicit, is essential to the process. We contribute methods for “latent space cartography”, the process of mapping and comparing meaningful semantic dimensions within latent spaces. We first perform a literature survey of relevant machine learning, natural language processing, and scientific research to distill common tasks and propose a workflow process. Next, we present an integrated visual analysis system for supporting this workflow, enabling users to discover, define, and verify meaningful relationships among data points, encoded within latent space dimensions. Three case studies demonstrate how users of our system can compare latent space variants in image generation, challenge existing findings on cancer transcriptomes, and assess a word embedding benchmark.

This article introduces Cartograph, a visualization system that harnesses the vast world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. Although these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph’s map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original datasets but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered. We present findings from two user studies exploring design choices and use of the system.

There are few large-scale empirical studies with people with disabilities or older adults, mainly because recruiting partici­pants with specific characteristics is even harder than recruit­ing young and/or non-disabled populations. Analyzing four online experiments on LabintheWild with a total of 355,656 participants, we show that volunteer-based online experiments that provide personalized feedback attract large numbers of participants with diverse disabilities and ages and allow ro­bust studies with these populations that replicate and extend the findings of prior laboratory studies. To find out what mo­tivates people with disabilities to take part, we additionally analyzed participants' feedback and forum entries that discuss LabintheWild experiments. The results show that participants use the studies to diagnose themselves, compare their abilities to others, quantify potential impairments, self-experiment, and share their own stories -- findings that we use to inform design guidelines for online experiment platforms that adequately support and engage people with disabilities.

This paper introduces Cartograph, a visualization system that harnesses the vast amount of world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. While these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph's map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original data sets, but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered, and we present findings from an exploratory user study