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Race, Disability and Accessibility Technology

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Working at the Intersection of Race, Disability, and Accessibility

This paper asks how research in accessibility can do a better job of including all disabled person, rather than separating disability from a person’s race and ethnicity. Most of the accessibility research that was published in the past does not mention race, or treats it as a simple label rather than asking how it impacts disability experiences. This eliminates whole areas of need and vital perspectives from the work we do.

We present a series of case studies exploring positive examples of work that looks more deeply at this intersection and reflect on teaching at the intersection of race, disability, and technology. This paper highlights the value of considering how constructs of race and disability work alongside each other within accessibility research studies, designs of socio-technical systems, and education. Our analysis provides recommendations towards establishing this research direction.

Christina N. Harrington, Aashaka Desai, Aaleyah Lewis, Sanika Moharana, Anne Spencer Ross, Jennifer Mankoff: Working at the Intersection of Race, Disability and Accessibility. ASSETS 2023: 26:1-26:18 (pdf)

https://youtube.com/watch?v=qRMYjdSTnZs%3Fsi%3D0yhLkUyGKu-WO4Na

Azimuth: Designing Accessible Dashboards for Screen Reader Users

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Dashboards are frequently used to monitor and share data across a breadth of domains including business, finance, sports, public policy, and healthcare, just to name a few. The combination of different components (e.g., key performance indicators, charts, filtering widgets) and the interactivity between components makes dashboards powerful interfaces for data monitoring and analysis. However, these very characteristics also often make dashboards inaccessible to blind and low vision (BLV) users. Through a co-design study with two screen reader users, we investigate challenges faced by BLV users and identify design goals to support effective screen reader-based interactions with dashboards. Operationalizing the findings from the co-design process, we present a prototype system, Azimuth, that generates dashboards optimized for screen reader-based navigation along with complementary descriptions to support dashboard comprehension and interaction. Based on a follow-up study with five BLV participants, we showcase how our generated dashboards support BLV users and enable them to perform both targeted and open-ended analysis. Reflecting on our design process and study feedback, we discuss opportunities for future work on supporting interactive data analysis, understanding dashboard accessibility at scale, and investigating alternative devices and modalities for designing accessible visualization dashboards.

Arjun Srinivasan, Tim Harshbarger, Darrell Hilliker and Jennifer Mankoff: University of Washington (2023): “Azimuth: Designing Accessible Dashboards for Screen Reader Users” ASSETS 2023.

The Role of Speechreading in Online d/DHH Communication Accessibility

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Speechreading is the art of using visual and contextual cues in the environment to support listening. Often used by d/Deaf and Hard-of-Hearing (d/DHH) individuals, it highlights nuances of rich communication. However, lived experiences of speechreaders are underdocumented in the literature, and the impact of online environment and interaction of captioning with speechreading has not been explored. To bridge these gaps, we conducted a three-part study consisting of formative interviews, design probes and design sessions with 12 d/DHH individuals who speechread.

Making a Medical Maker’s Playbook: An Ethnographic Study of Safety-Critical Collective Design by Makers in Response to COVID-19

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Megan Hofmann, Udaya Lakshmi, Kelly Mack, Rosa I. Arriaga, Scott E. Hudson, and Jennifer Mankoff. Making a Medical Maker’s Playbook: An Ethnographic Study of Safety-Critical Collective Design by Makers in Response to COVID-19. Proc. ACM Hum. Comput. Interact. 6(CSCW1): 101:1-101:26 (2022).

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.

Cross-Dataset Generalization for Human Behavior Modeling

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Overview; Data; Code

Overview of The Contributions of This Work. We systematically evaluate cross-dataset generalizability of 19 algorithms: 9 prior behavior modeling algorithm for depression detection, 8 recent domain generalization algorithms, and 2 two new algorithms proposed in this paper. Our open-source platform GLOBEM consolidates these 19 algorithms and support using, developing, evaluating various algorithms.

There is a growing body of research revealing that longitudinal passive sensing data from smartphones and wearable devices can capture daily behavior signals for human behavior modeling, such as depression detection. Most prior studies build and evaluate machine learning models using data collected from a single population. However, to ensure that a behavior model can work for a larger group of users, its generalizability needs to be verified on multiple datasets from different populations. We present the first work evaluating cross-dataset generalizability of longitudinal behavior models, using depression detection as an application. We collect multiple longitudinal passive mobile sensing datasets with over 500 users from two institutes over a two-year span, leading to four institute-year datasets. Using the datasets, we closely re-implement and evaluated nine prior depression detection algorithms. Our experiment reveals the lack of model generalizability of these methods. We also implement eight recently popular domain generalization algorithms from the machine learning community. Our results indicate that these methods also do not generalize well on our datasets, with barely any advantage over the naive baseline of guessing the majority. We then present two new algorithms with better generalizability. Our new algorithm, Reorder, significantly and consistently outperforms existing methods on most cross-dataset generalization setups. However, the overall advantage is incremental and still has great room for improvement. Our analysis reveals that the individual differences (both within and between populations) may play the most important role in the cross-dataset generalization challenge. Finally, we provide an open-source benchmark platform GLOBEM – short for Generalization of LOngitudinal BEhavior Modeling – to consolidate all 19 algorithms. GLOBEM can support researchers in using, developing, and evaluating different longitudinal behavior modeling methods. We call for researchers’ attention to model generalizability evaluation for future longitudinal human behavior modeling studies.

Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman S. Sefidgar, Woosuk Seo, Kevin S. Kuehn, Jeremy F. Huckins, Margaret E. Morris, Paula S. Nurius, Eve A. Riskin, Shwetak N. Patel, Tim Althoff, Andrew Campbell, Anind K. Dey, and Jennifer Mankoff. GlOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(4): 190:1-190:34 (2022).

Xuhai Xu, Han Zhang, Yasaman S. Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin S. Kuehn, Mike A. Merrill, Paula S. Nurius, Shwetak N. Patel, Tim Althoff, Margaret Morris, Eve A. Riskin, Jennifer Mankoff, Anind K. Dey:
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. NeurIPS 2022

PSST: Enabling Blind or Visually Impaired Developers to Author Sonifications of Streaming Sensor Data

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Venkatesh Potluri, John Thompson, James Devine, Bongshin Lee, Nora Morsi, Peli De Halleux, Steve Hodges, and Jennifer Mankoff. 2022. PSST: Enabling Blind or Visually Impaired Developers to Author Sonifications of Streaming Sensor Data. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (UIST ’22). Association for Computing Machinery, New York, NY, USA, Article 46, 1–13. https://doi.org/10.1145/3526113.3545700

We present the first toolkit that equips blind and visually impaired (BVI) developers with the tools to create accessible data displays. Called PSST (Physical Computing Streaming Sensor data Toolkit), it enables BVI developers to understand the data generated by sensors from a mouse to a micro: bit physical computing platform. By assuming visual abilities, earlier efforts to make physical computing accessible fail to address the need for BVI developers to access sensor data. PSST enables BVI developers to understand real-time, real-world sensor data by providing control over what should be displayed, as well as when to display and how to display sensor data. PSST supports filtering based on raw or calculated values, highlighting, and transformation of data. Output formats include tonal sonification, nonspeech audio files, speech, and SVGs for laser cutting. We validate PSST through a series of demonstrations and a user study with BVI developers.

The demo video can be found here: https://youtu.be/UDIl9krawxg.

Rapid Convergence: The Outcomes of Making PPE during a Healthcare Crisis

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Kelly Avery Mack, Megan Hofmann, Udaya Lakshmi, Jerry Cao, Nayha Auradkar, Rosa I. Arriaga, Scott E. Hudson, Jennifer Mankoff. Rapid Convergence: The Outcomes of Making PPE During a Healthcare Crisis. [Link to the paper]

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.

Personalized behavior modeling: depression detection

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Xuhai Xu, Prerna Chikersal, Janine M. Dutcher, Yasaman S. Sefidgar, Seo Woosuk, Michael J. Tumminia, Daniella K. Villalba, Sheldon Cohen,
Kasey G. Creswell, Creswell, J. David, Afsaneh Doryab, Paula S. Nurius, Eve Riskin, Anind K. Dey, & Jennifer Mankoff. Leveraging Collaborative-Filtering for Personalized Behavior Modeling: A Case Study of Depression Detection among College Students. Proc. ACM interact. mob. wearable ubiquitous technol., Article 41. (March 21) 27pages.

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interoperability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.

Distress and resilience among marginalized undergraduates

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Nurius, P. S., Sefidgar, Y. S., Kuehn, K. S, Jake, X, Zhang, H., Browning, A., Riskin, E., Dey, A. K., & Mankoff, J.  Distress among undergraduates: Marginality, stressors and resilience supports. Journal of American College Health, 1-9.

Stress and related mental health struggles are of growing concern at colleges and universities across the country and internationally, with some evidence of levels higher than general population peers. The university experience can pose considerable strain on students, in some cases adding to early and current life stressors, and, if not mitigated, can lead to impaired well-being and academic success/retention.

This study provides a 2019 data snapshot of multiple stressor effects on early-stage students, resilience resources (or the lack thereof) that can mitigate these effects, and sociodemographic characteristics reflecting minoritized identities. Participants were 253 first- and second-year undergraduate students (age =18.76; 49.80% male, 69% students of color) enrolled at the University of Washington.

Multivariate analysis demonstrated significant associations between greater stress exposures and lower levels of resilience resources with each of three mental health indicators—perceived stress (intensity of experienced stress), depression, and anxiety. Stressors such as poor physical health, discrimination exposure, experiencing one or more marginalizing status (e.g., first generation student, having disabilities, sexual minority), and using maladaptive coping strategies (e.g, denial, self-blame) significantly accounted for each of the mental health indicators. Prior stressors such as adverse childhood experiences and other life and academic adversities were also significantly correlated with the mental health variables.

Race/ethnicity was less clearly patterned, although students of Asian descent reported significantly greater depression and anxiety, and females reported higher levels on all distress forms. In terms of resilience supports, those reporting greater social support and perception of oneself as a “bounce back” kind of person reported lesser psychological distress and these variables reduced the effects of stressors. Assessment of student well-being from this same project during the 2020 COVID-19 context indicated that students entering the pandemic with mental health vulnerabilities experienced significantly greater psychological distress and academic strain as the university pivoted toward remote instruction, signaling highly consequential differences (Morris et al., 2021)

These results support the value of “poly-strengths” –multiple forms of resilience- fostering resources–for mitigating the effects of stressors on psychological distress. College leaders are noting increases in the severity of students’ mental health concerns and demand for services, changing the roles of campus counseling centers, and requiring new institutional responses. Better understanding cumulative stress/resilience resource profiles, particularly among marginalized students and those experiencing discrimination, can help universities in prioritizing institutional support responses toward prevention, strengthening resilience, and mitigating psychological distress.

COVID-19 and Remote Learning for Students with Disabilities

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Han Zhang, Margaret E. Morris, Paula S. Nurius, Kelly Mack, Jennifer Brown, Kevin S. Kuehn, Yasaman S. Sefidgar, Xuhai Xu, Eve A. Riskin, Anind K. Dey and Jennifer Mankoff. Impact of Online Learning in the Context of COVID-19 on Undergraduates with Disabilities and Mental Health Concerns. ACM Transactions on Accessible Computing (TACCESS).

The COVID-19 pandemic upended college education and the experiences of students due to the rapid and uneven shift to online learning. This study examined the experiences of students with disabilities with online learning, with a consideration of surrounding stressors such as financial pressures. In a mixed method approach, we compared 28 undergraduate students with disabilities(including mental health concerns) to their peers during 2020, to assess differences and similarities in their educational concerns, stress levels and COVID-19 related adversities. We found that students with disabilities entered the Spring quarter of 2020 with significantly higher concerns about classes going online, and reported more recent negative life events than other students. These differences between the two groups diminished three months later with the exception of recent negative life events. For a fuller understanding of students’ experiences, we conducted qualitative analysis of open ended interviews. We examined both positive and negative experiences with online learning among students with disabilities and mental health concerns. Online learning enabled greater access – e.g., reducing the need for travel to campus–alongside ways in which online learning impeded academic engagement–e.g., reducing interpersonal interaction. Learning systems need to continue to meet the diverse and dynamic needs of students with disabilities.