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Notably Inaccessible

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Venkatesh Potluri, Sudheesh Singanamalla, Nussara Tieanklin, Jennifer Mankoff: Notably Inaccessible – Data Driven Understanding of Data Science Notebook (In)Accessibility. ASSETS 2023: 13:1-13:19

Computational notebooks are tools that help people explore, analyze data, and create stories about that data. They are the most popular choice for data scientists. People use software like Jupyter, Datalore, and Google Colab to work with these notebooks in universities and companies.

There is a lot of research on how data scientists use these notebooks and how to help them work together better. But there is not much information about the problems faced by blind and visually impaired (BVI) users. BVI users have difficulty using these notebooks because:

  • The interfaces are not accessible.
  • The way data is shown is not user-friendly for them.
  • Popular libraries do not provide outputs they can use.

We analyzed 100,000 Jupyter notebooks to find accessibility problems. We looked for issues that affect how these notebooks are created and read. From our study, we give advice on how to make notebooks more accessible. We suggest ways for people to write better notebooks and changes to make the notebook software work better for everyone.

Touchpad Mapper

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Ather Sharif, Venkatesh Potluri, Jazz Rui Xia Ang, Jacob O. Wobbrock, Jennifer Mankoff: Touchpad Mapper: Examining Information Consumption From 2D Digital Content Using Touchpads by Screen-Reader Users: ASSETS ’24 (best poster!) and W4A ’24 (open access)

Touchpads are common, but they are not very useful for people who use screen readers. We created and tested a tool called Touchpad Mapper to let Blind and visually impaired people make better use of touchpads. Touchpad Mapper lets screen-reader users use touchpads to interact with digital content like images and videos.

Touchpad mapping could be used in many apps. We built two examples:

  1. Users can use the touchpad to identify where things are in an image.
  2. Users can control a video’s progress with the touchpad, including rewinding and fast-forwarding.

We tested Touchpad Mapper with three people who use screen readers. They said they got information more quickly with our tool than with a regular keyboard.

Bespoke Slides for Fluctuating Access Needs

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Kelly Avery Mack, Kate S. Glazko, Jamil Islam, Megan Hofmann, Jennifer Mankoff: “It’s like Goldilocks: ” Bespoke Slides for Fluctuating Audience Access Needs. ASSETS 2024: 71:1-71:15

Slide deck accessibility is usually thought to mainly impact people who are blind or visually impaired. However, many other people might need modifications to access a slide deck.

We talked with 17 people who have disabilities and use slide decks and learned their needs did not always overlap. For some people, their own needs even changed at times. For example, some needed lower contrast colors at night.

Next, we explored how a tool could help change a presentation to fit their needs. We tested this tool with 14 of the people we talked to earlier. Then, we interviewed four people who make and present slide decks to get their thoughts on this tool.

Finally, we tried to make a working version of this tool. It has some of the features that the people we talked to wanted, but we learned that when apps are not designed for access, and not open source, they make full access hard to add.

Generative Artificial Intelligence’s Utility for Accessibility

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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.

Glazko, K. S., Yamagami, M., Desai, A., Mack, K. A., Potluri, V., Xu, X., & Mankoff, J. An Autoethnographic Case Study of Generative Artificial Intelligence’s Utility for Accessibility. ASSETS 2023. https://dl.acm.org/doi/abs/10.1145/3597638.3614548

News: Can AI help boost accessibility? These researchers tested it for themselves

Presentation (starts at about 20mins)

https://youtube.com/watch?v=S40-jPBH820%3Fsi%3DCm17oTaMaDnoQGvK%3F%23t%3D20m26s

How Do People with Limited Movement Personalize Upper-Body Gestures?

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Personalized upper-body gestures that can enable input from diverse body parts (e.g., head, neck, shoulders, arms, hands, and fingers), and match the abilities of each user, might make gesture systems more accessible for people with upper-body motor disabilities. Static gesture sets that make ability assumptions about the user (e.g., touch thumb and index finger together in midair) may not be accessible. In our work, we characterize the personalized gesture sets designed by 25 participants with upper-body motor disabilities. We found that the personalized gesture sets that participants designed were specific to their abilities and needs. Six participants mentioned that their inspiration for designing the gestures was based on “how I would do [the gesture] with the abilities that I have”. We suggest three considerations when designing accessible upper-body gesture interfaces: 

1) Track the whole upper body. Our participants used their whole upper-body to perform the gestures, and some switched back and forth from the left to the right hand to combat fatigue.

2) Use sensing mechanisms that are agnostic to the location and orientation of the body. About half of our participants kept their hand on or barely took their hand off of the armrest to decrease arm movement and fatigue.

3) Use sensors that can sense muscle activations without movement. Our participants activated their muscles but did not visibly move in 10% of the personalized gestures.   

Our work highlights the need for personalized upper-body gesture interfaces supported by multimodal biosignal sensors (e.g., accelerometers, sensors that can sense muscle activity like EMG). 

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.

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.