Jonathan Lazar

Blind and low-vision individuals encounter significant challenges in accessing and interpreting data, a critical component in many data-intensive fields. Traditional screen readers, which convert text to speech, have been the primary solution, but recent developments such as the Monarch refreshable tactile display and hybrid approaches such as TactualPlot, which combines sound and touch, offer new possibilities for data accessibility. In this article, we present a controlled, within-subject comparative study of three accessible data exploration systems: a screen-reader-based chart description interface (Olli), a tablet-based audio-tactile system (TactualPlot), and a multi-line refreshable tactile display (Monarch). Ten blind participants completed data analysis tasks involving bar charts, pie charts, line charts, and scatterplots, spanning single-point identification, pairwise comparison, and trend analysis. We evaluated performance using task accuracy and completion time, subjective workload using NASA-TLX, and qualitative feedback through think-aloud protocols. Results indicate that task accuracy did not differ significantly across systems, while completion time varied substantially, with participants completing tasks fastest on the refreshable tactile display. NASA-TLX ratings revealed significant differences in perceived workload across systems, particularly for mental demand, effort, and temporal demand. Qualitative findings highlight how system-specific interaction techniques, hardware constraints, and feedback mappings shape user strategies, learning curves, and perceived efficiency, especially for complex chart types such as line charts and scatterplots. Rather than isolating sensory modalities in abstraction, our findings demonstrate how accessible data exploration is mediated by device-level design choices and interaction techniques that operationalize audio and touch in practice. We conclude with design implications for accessible visualization systems and guidance for future research on integrating audio, tactile, and speech-based interaction techniques to better support non-visual data analysis.

PDF documents are usually not born-accessible, and so document authors need to put in additional work (remediation) to make them accessible for people with disabilities. Unfortunately, this step is often overlooked and hard to execute, resulting in a large number of inaccessible PDF documents on the internet. Previously, there have been research efforts to investigate potential solutions for remediating PDF documents for accessibility. However, most of the existing research focuses on accessibility of long or scientific PDF documents meant for passive reading. PDF documents come in different types, and this research project focuses on a distinct type of PDF document—forms—where the user is required to interact with the PDF document and enter data. Through our research work we identified that the PDF form remediation process is non-intuitive, repetitive, and overwhelming due to the high-information density of PDF forms, and existing research and tools do not yet address the challenges. Our research work culminated in the creation of a tool – FormA11y – that addresses these challenges by making the repetitive and painstaking process of form remediation easier. To evaluate the effectiveness and efficiency of FormA11y against the industry standard tool – Adobe Acrobat – for PDF form remediation, we performed a within-subject user study with 20 participants. With FormA11y, users remediated forms 2.8 times faster while creating more accurately accessible PDF forms.

Seeking to improve AI accessibility by changing how AI-based systems are built.

Inclusivity for blind and low vision (BLV) professionals in data science and analytics is limited by a gap in understanding their unique data analysis needs. We contribute to the literature by reporting on a two-step online survey delving into the experiences and challenges faced by BLV individuals engaged in data-related roles. Our findings highlight that despite expertise in programming and GUI-based analysis tools, BLV professionals faced accessibility issues at various points in the data analysis pipeline—issues ranging from data loading and transformation, availability and compatibility of data tools with assistive technology, and visualization authoring. The prevalent use of tools such as Excel, Python, and SAS alongside heavy reliance on assistive technologies highlights persistent accessibility challenges. Furthermore, frequent collaboration with sighted colleagues indicates compromised independence. These results underscore the urgent need for “born accessible” tools that ensure the inclusivity and autonomy of BLV professionals in the field of data science.

As health management becomes more intertwined with data, an individual’s ability to read, interpret, and engage with personal health information in data visualizations is increasingly critical to one’s quality of care. People with Down Syndrome already experience greater health disparities than their typically developing peers. Inaccessible health information and technologies have the potential to magnify inequities further. Inaccessible health data can be an additional barrier to people with Down Syndrome’s ability to adopt and use health systems or devices, make informed decisions about their bodies, and advocate for themselves in health contexts. By examining their underlying data visualization literacy skills, our exploratory study involving ten young adults with Down Syndrome identifies several design opportunities to improve the accessibility of health data visualizations (HDVs) by addressing the cascade of negative effects caused by inference-making barriers in HDVs.