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    New software enables blind and low-vision users to create interactive, accessible charts

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    A growing number of tools enable users to make online data representations, like charts, that are accessible for people who are blind or have low vision. However, most tools require an existing visual chart that can then be converted into an accessible format.

    This creates barriers that prevent blind and low-vision users from building their own custom data representations, and it can limit their ability to explore and analyze important information.

    A team of researchers from MIT and University College London (UCL) wants to change the way people think about accessible data representations.

    They created a software system called Umwelt (which means “environment” in German) that can enable blind and low-vision users to build customized, multimodal data representations without needing an initial visual chart.

    Umwelt, an authoring environment designed for screen-reader users, incorporates an editor that allows someone to upload a dataset and create a customized representation, such as a scatterplot, that can include three modalities: visualization, textual description, and sonification. Sonification involves converting data into nonspeech audio.

    The system, which can represent a variety of data types, includes a viewer that enables a blind or low-vision user to interactively explore a data representation, seamlessly switching between each modality to interact with data in a different way.

    The researchers conducted a study with five expert screen-reader users who found Umwelt to be useful and easy to learn. In addition to offering an interface that empowered them to create data representations — something they said was sorely lacking — the users said Umwelt could facilitate communication between people who rely on different senses.

    “We have to remember that blind and low-vision people aren’t isolated. They exist in these contexts where they want to talk to other people about data,” says Jonathan Zong, an electrical engineering and computer science (EECS) graduate student and lead author of a paper introducing Umwelt. “I am hopeful that Umwelt helps shift the way that researchers think about accessible data analysis. Enabling the full participation of blind and low-vision people in data analysis involves seeing visualization as just one piece of this bigger, multisensory puzzle.”

    Joining Zong on the paper are fellow EECS graduate students Isabella Pedraza Pineros and Mengzhu “Katie” Chen; Daniel Hajas, a UCL researcher who works with the Global Disability Innovation Hub; and senior author Arvind Satyanarayan, associate professor of computer science at MIT who leads the Visualization Group in the Computer Science and Artificial Intelligence Laboratory. The paper will be presented at the ACM Conference on Human Factors in Computing.

    De-centering visualization

    The researchers previously developed interactive interfaces that provide a richer experience for screen reader users as they explore accessible data representations. Through that work, they realized most tools for creating such representations involve converting existing visual charts.

    Aiming to decenter visual representations in data analysis, Zong and Hajas, who lost his sight at age 16, began co-designing Umwelt more than a year ago.

    At the outset, they realized they would need to rethink how to represent the same data using visual, auditory, and textual forms.

    “We had to put a common denominator behind the three modalities. By creating this new language for representations, and making the output and input accessible, the whole is greater than the sum of its parts,” says Hajas.

    To build Umwelt, they first considered what is unique about the way people use each sense.

    For instance, a sighted user can see the overall pattern of a scatterplot and, at the same time, move their eyes to focus on different data points. But for someone listening to a sonification, the experience is linear since data are converted into tones that must be played back one at a time.

    “If you are only thinking about directly translating visual features into nonvisual features, then you miss out on the unique strengths and weaknesses of each modality,” Zong adds.

    They designed Umwelt to offer flexibility, enabling a user to switch between modalities easily when one would better suit their task at a given time.

    To use the editor, one uploads a dataset to Umwelt, which employs heuristics to automatically creates default representations in each modality.

    If the dataset contains stock prices for companies, Umwelt might generate a multiseries line chart, a textual structure that groups data by ticker symbol and date, and a sonification that uses tone length to represent the price for each date, arranged by ticker symbol.

    The default heuristics are intended to help the user get started.

    “In any kind of creative tool, you have a blank-slate effect where it is hard to know how to begin. That is compounded in a multimodal tool because you have to specify things in three different representations,” Zong says.

    The editor links interactions across modalities, so if a user changes the textual description, that information is adjusted in the corresponding sonification. Someone could utilize the editor to build a multimodal representation, switch to the viewer for an initial exploration, then return to the editor to make adjustments.

    Helping users communicate about data

    To test Umwelt, they created a diverse set of multimodal representations, from scatterplots to multiview charts, to ensure the system could effectively represent different data types. Then they put the tool in the hands of five expert screen reader users.

    Study participants mostly found Umwelt to be useful for creating, exploring, and discussing data representations. One user said Umwelt was like an “enabler” that decreased the time it took them to analyze data. The users agreed that Umwelt could help them communicate about data more easily with sighted colleagues.

    “What stands out about Umwelt is its core philosophy of de-emphasizing the visual in favor of a balanced, multisensory data experience. Often, nonvisual data representations are relegated to the status of secondary considerations, mere add-ons to their visual counterparts. However, visualization is merely one aspect of data representation. I appreciate their efforts in shifting this perception and embracing a more inclusive approach to data science,” says JooYoung Seo, an assistant professor in the School of Information Sciences at the University of Illinois at Urbana-Champagne, who was not involved with this work.

    Moving forward, the researchers plan to create an open-source version of Umwelt that others can build upon. They also want to integrate tactile sensing into the software system as an additional modality, enabling the use of tools like refreshable tactile graphics displays.

    “In addition to its impact on end users, I am hoping that Umwelt can be a platform for asking scientific questions around how people use and perceive multimodal representations, and how we can improve the design beyond this initial step,” says Zong.

    This work was supported, in part, by the National Science Foundation and the MIT Morningside Academy for Design Fellowship. More

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      Q&A: How refusal can be an act of design

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      This month in the ACM Journal on Responsible Computing, MIT graduate student Jonathan Zong SM ’20 and co-author J. Nathan Matias SM ’13, PhD ’17 of the Cornell Citizens and Technology Lab examine how the notion of refusal can open new avenues in the field of data ethics. In their open-access report, “Data Refusal From Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design,” the pair proposes a framework in four dimensions to map how individuals can say “no” to technology misuses. At the same time, the researchers argue that just like design, refusal is generative, and has the potential to create alternate futures.

      Zong, a PhD candidate in electrical engineering and computer science, 2022-23 MIT Morningside Academy for Design Design Fellow, and member of the MIT Visualization Group, describes his latest work in this Q&A.

      Q: How do you define the concept of “refusal,” and where does it come from?

      A: Refusal was developed in feminist and Indigenous studies. It’s this idea of saying “no,” without being given permission to say “no.” Scholars like Ruha Benjamin write about refusal in the context of surveillance, race, and bioethics, and talk about it as a necessary counterpart to consent. Others, like the authors of the “Feminist Data Manifest-No,” think of refusal as something that can help us commit to building better futures.

      Benjamin illustrates cases where the choice to refuse is not equally possible for everyone, citing examples involving genetic data and refugee screenings in the U.K. The imbalance of power in these situations underscores the broader concept of refusal, extending beyond rejecting specific options to challenging the entire set of choices presented.

      Q: What inspired you to work on the notion of refusal as an act of design?

      A: In my work on data ethics, I’ve been thinking about how to incorporate processes into research data collection, particularly around consent and opt-out, with a focus on individual autonomy and the idea of giving people choices about the way that their data is used. But when it comes to data privacy, simply making choices available is not enough. Choices can be unequally available, or create no-win situations where all options are bad. This led me to the concept of refusal: questioning the authority of data collectors and challenging their legitimacy.

      The key idea of my work is that refusal is an act of design. I think of refusal as deliberate actions to redesign our socio-technical landscape by exerting some sort of influence. Like design, refusal is generative. Like design, it’s oriented towards creating alternate possibilities and alternate futures. Design is a process of exploring or traversing a space of possibility. Applying a design framework to cases of refusal drawn from scholarly and journalistic sources allowed me to establish a common language for talking about refusal and to imagine refusals that haven’t been explored yet.

      Q: What are the stakes around data privacy and data collection?

      A: The use of data for facial recognition surveillance in the U.S. is a big example we use in the paper. When people do everyday things like post on social media or walk past cameras in public spaces, they might be contributing their data to training facial recognition systems. For instance, a tech company may take photos from a social media site and build facial recognition that they then sell to the government. In the U.S., these systems are disproportionately used by police to surveil communities of color. It is difficult to apply concepts like consent and opt out of these processes, because they happen over time and involve multiple kinds of institutions. It’s also not clear that individual opt-out would do anything to change the overall situation. Refusal then becomes a crucial avenue, at both individual and community levels, to think more broadly of how affected people still exert some kind of voice or agency, without necessarily having an official channel to do so.

      Q: Why do you think these issues are more particularly affecting disempowered communities?

      A: People who are affected by technologies are not always included in the design process for those technologies. Refusal then becomes a meaningful expression of values and priorities for those who were not part of the early design conversations. Actions taken against technologies like face surveillance — be it legal battles against companies, advocacy for stricter regulations, or even direct action like disabling security cameras — may not fit the conventional notion of participating in a design process. And yet, these are the actions available to refusers who may be excluded from other forms of participation.

      I’m particularly inspired by the movement around Indigenous data sovereignty. Organizations like the First Nations Information Governance Centre work towards prioritizing Indigenous communities’ perspectives in data collection, and refuse inadequate representation in official health data from the Canadian government. I think this is a movement that exemplifies the potential of refusal, not only as a way to reject what’s being offered, but also as a means to propose a constructive alternative, very much like design. Refusal is not merely a negation, but a pathway to different futures.

      Q: Can you elaborate on the design framework you propose?

      A: Refusals vary widely across contexts and scales. Developing a framework for refusal is about helping people see actions that are seemingly very different as instances of the same broader idea. Our framework consists of four facets: autonomy, time, power, and cost.

      Consider the case of IBM creating a facial recognition dataset using people’s photos without consent. We saw multiple forms of refusal emerge in response. IBM allowed individuals to opt out by withdrawing their photos. People collectively refused by creating a class-action lawsuit against IBM. Around the same time, many U.S. cities started passing local legislation banning the government use of facial recognition. Evaluating these cases through the framework highlights commonalities and differences. The framework highlights varied approaches to autonomy, like individual opt-out and collective action. Regarding time, opt-outs and lawsuits react to past harm, while legislation might proactively prevent future harm. Power dynamics differ; withdrawing individual photos minimally influences IBM, while legislation could potentially cause longer-term change. And as for cost, individual opt-out seems less demanding, while other approaches require more time and effort, balanced against potential benefits.

      The framework facilitates case description and comparison across these dimensions. I think its generative nature encourages exploration of novel forms of refusal as well. By identifying the characteristics we want to see in future refusal strategies — collective, proactive, powerful, low-cost… — we can aspire to shape future approaches and change the behavior of data collectors. We may not always be able to combine all these criteria, but the framework provides a means to articulate our aspirational goals in this context.

      Q: What impact do you hope this research will have?

      A: I hope to expand the notion of who can participate in design, and whose actions are seen as legitimate expressions of design input. I think a lot of work so far in the conversation around data ethics prioritizes the perspective of computer scientists who are trying to design better systems, at the expense of the perspective of people for whom the systems are not currently working. So, I hope designers and computer scientists can embrace the concept of refusal as a legitimate form of design, and a source of inspiration. There’s a vital conversation happening, one that should influence the design of future systems, even if expressed through unconventional means.

      One of the things I want to underscore in the paper is that design extends beyond software. Taking a socio-technical perspective, the act of designing encompasses software, institutions, relationships, and governance structures surrounding data use. I want people who aren’t software engineers, like policymakers or activists, to view themselves as integral to the technology design process. More

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      in Data Management & Statistics

      Improving accessibility of online graphics for blind users

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      The beauty of a nice infographic published alongside a news or magazine story is that it makes numeric data more accessible to the average reader. But for blind and visually impaired users, such graphics often have the opposite effect.

      For visually impaired users — who frequently rely on screen-reading software that speaks words or numbers aloud as the user moves a cursor across the screen — a graphic may be nothing more than a few words of alt text, such as a chart’s title. For instance, a map of the United States displaying population rates by county might have alt text in the HTML that says simply, “A map of the United States with population rates by county.” The data has been buried in an image, making it entirely inaccessible.

      “Charts have these various visual features that, as a [sighted] reader, you can shift your attention around, look at high-level patterns, look at individual data points, and you can do this on the fly,” says Jonathan Zong, a 2022 MIT Morningside Academy for Design (MAD) Fellow and PhD student in computer science, who points out that even when a graphic includes alt text that interprets the data, the visually impaired user must accept the findings as presented.

      “If you’re [blind and] using a screen reader, the text description imposes a linear predefined reading order. So, you’re beholden to the decisions that the person who wrote the text made about what information was important to include.”

      While some graphics do include data tables that a screen reader can read, it requires the user to remember all the data from each row and column as they move on to the next one. According to the National Federation of the Blind, Zong says, there are 7 million people living in the United States with visual disabilities, and nearly 97 percent of top-level pages on the internet are not accessible to screen readers. The problem, he points out, is an especially difficult one for blind researchers to get around. Some researchers with visual impairments rely on a sighted collaborator to read and help interpret graphics in peer-reviewed research.

      Working with the Visualization Group at the Computer Science and Artificial Intelligence Lab (CSAIL) on a project led by Associate Professor Arvind Satyanarayan that includes Daniel Hajas, a blind researcher and innovation manager at the Global Disability Innovation Hub in England, Zong and others have written an open-source Javascript software program named Olli that solves this problem when it’s included on a website. Olli is able to go from big-picture analysis of a chart to the finest grain of detail to give the user the ability to select the degree of granularity that interests them.

      “We want to design richer screen-reader experiences for visualization with a hierarchical structure, multiple ways to navigate, and descriptions at varying levels of granularity to provide self-guided, open-ended exploration for the user.”

      Next steps with Olli are incorporating multi-sensory software to integrate text and visuals with sound, such as having a musical note that moves up or down the harmonic scale to indicate the direction of data on a linear graph, and possibly even developing tactile interpretations of data. Like most of the MAD Fellows, Zong integrates his science and engineering skills with design and art to create solutions to real-world problems affecting individuals. He’s been recognized for his work in both the visual arts and computer science. He holds undergraduate degrees in computer science and visual arts with a focus on graphic design from Princeton University, where his research was on the ethics of data collection.

      “The throughline is the idea that design can help us make progress on really tough social and ethical questions,” Zong says, calling software for accessible data visualization an “intellectually rich area for design.” “We’re thinking about ways to translate charts and graphs into text descriptions that can get read aloud as speech, or thinking about other kinds of audio mappings to sonify data, and we’re even exploring some tactile methods to understand data,” he says.

      “I get really excited about design when it’s a way to both create things that are useful to people in everyday life and also make progress on larger conversations about technology and society. I think working in accessibility is a great way to do that.”

      Another problem at the intersection of technology and society is the ethics of taking user data from social media for large-scale studies without the users’ awareness. While working as a summer graduate research fellow at Cornell’s Citizens and Technology Lab, Zong helped create an open-source software called Bartleby that can be used in large anonymous data research studies. After researchers collect data, but before analysis, Bartleby would automatically send an email message to every user whose data was included, alert them to that fact and offer them the choice to review the resulting data table and opt out of the study. Bartleby was honored in the student category of Fast Company’s Innovation by Design Awards for 2022. In November the same year, Forbes magazine named Jonathan Zong in its Forbes 30 Under 30 in Science 2023 list for his work in data visualization accessibility.

      The underlying theme to all Zong’s work is the exploration of autonomy and agency, even in his artwork, which is heavily inclusive of text and semiotic play. In “Public Display,” he created a handmade digital display font by erasing parts of celebrity faces that were taken from a facial recognition dataset. The piece was exhibited in 2020 in MIT’s Wiesner Gallery, and received the third-place prize in the MIT Schnitzer Prize in the Visual Arts that year. The work deals not only with the neurological aspects of distinguishing faces from typefaces, but also with the implications for erasing individuals’ identities through the practice of using facial recognition programs that often target individuals in communities of color in unfair ways. Another of his works, “Biometric Sans,” a typography system that stretches letters based on a person’s typing speed, will be included in a show at the Harvard Science Center sometime next fall.

      “MAD, particularly the large events MAD jointly hosted, played a really important function in showing the rest of MIT that this is the kind of work we value. This is what design can look like and is capable of doing. I think it all contributes to that culture shift where this kind of interdisciplinary work can be valued, recognized, and serve the public.

      “There are shared ideas around embodiment and representation that tie these different pursuits together for me,” Zong says. “In the ethics work, and the art on surveillance, I’m thinking about whether data collectors are representing people the way they want to be seen through data. And similarly, the accessibility work is about whether we can make systems that are flexible to the way people want to use them.” More