Data is everywhere, influencing people’s decisions in various aspects---from business and education to healthcare and everyday life. By experiencing data , we refer to an individual's ability to independently collect, access, understand, interpret, represent, and share data and insights. However, not everyone can fully engage with or benefit from data today, due to inaccessible data representations such as visualizations and tools that fail to accommodate diverse abilities or levels of data literacy [1]. In particular, people who have disabilities such as blindness or low vision (BLV), cognitive and learning disabilities (CALD) or lack fine motor coordination are often excluded, a concern that is expected to grow with aging populations. This poses a significant equity issue, as data has become increasingly central not only to employment and education, but also to health, well-being, and daily living. Improving access to data and inclusion in data activities is crucial for ensuring equitable opportunities for all individuals and fostering a more inclusive, data-driven society.

Despite this growing importance, research on inclusive data experiences remains limited. Only a small number of researchers are actively examining how to make data experiences more inclusive. It is important to note that the study of inclusive data experiences is inherently multidisciplinary. While it lies at the intersection of assistive technology and human-data interaction, it also requires collaboration with researchers in other fields such as data visualization, education, health, personal informatics, and AI, to name a few. The Shonan Meeting offers an ideal environment to bring together researchers across these communities, fostering collaboration and advancing a shared agenda for inclusive data experiences.

Inclusive data experiences span a broad set of topics that need to be addressed to ensure equitable access to and meaningful engagement with data. Key areas include:

• Inclusive Data Representations : A central concern is how to represent data in ways that allow people to meaningfully interact with and understand it. One promising approach is to combine multiple modalities such as visual, auditory, and tactile channels. However, to realize full potential, we need evidence-based guidelines for multi-modal data representations grounded in empirical research with diverse user groups.
• Inclusive Data Analysis Tools : We need to design and develop tools that facilitate data analysis for people with diverse abilities. How can we design tools that support appropriate data representations and interaction techniques while catering for different levels of data and visual literacy?
• Technologies for Inclusive Data Experiences: Inclusive data experiences go beyond visual access to enable equitable participation in data activities. Advancements in technologies such as tactile and haptic displays, voice interfaces, and wearables offer new opportunities for people with disabilities to interact with data. These technologies can complement traditional screens and provide alternative sensory channels.

• Challenges & Opportunities of AI for Inclusive Data Experiences: AI holds great potential to enable more inclusive data experiences, for instance, by supporting natural language interaction and explanations. However, it also raises challenges around bias, explainability, and accessibility. Ensuring that AI-powered systems support diverse users fairly while enhancing inclusive data engagement is an important area for research.

•  Real-world Data Needs: To create inclusive data experiences, it is essential to understand how people with disabilities engage with data in their daily lives. Research must center their voices and lived experiences to uncover unmet needs, contextual barriers, and opportunities.

• Personal Informatics: We increasingly encounter data in our personal lives, not only at work or school. Personal informatics systems need to be reenvisioned to accommodate different abilities and life contexts, enabling people to interact with and understand personal data related to health, finance, productivity, lifestyle, navigation, and more.

• Data Literacy: Most current data literacy education materials are not accessible, excluding many people with disabilities from obtaining data skills. There is an urgent need to ensure equitable access to data literacy education for all, tailored to individuals’ diverse abilities and needs.

This seminar builds on two previous workshops that have primarily focused on making data visualizations more accessible to people with disabilities. Lee and Marriott organized a workshop on Inclusive Data Visualization at Microsoft Research, Redmond in 2020, followed by a Dagstuhl Seminar on Inclusive Data Visualization in 2023 [2]. These earlier efforts highlighted the need to consider data experiences beyond just visualization and the importance of addressing a wider range of accessibility considerations, including the needs of individuals with limited literacy. Prior research has predominantly focused on the needs of people who are blind or have low vision, with inclusion of other communities needing more consideration.

Building on this foundation, this Shonan meeting will bring together experts from the related research areas including accessibility, data visualization, personal informatics, human-computer interaction, and AI to:

• Define this emerging area of inclusive data experiences and its different dimensions;
• Identify a research agenda and formulate a set of grand challenges for future work, building on those proposed at the 2023 Dagstuhl Seminar on Inclusive Data Visualization;
• Foster a collaborative network of researchers dedicated to inclusive data experiences;

• Develop an online resource hub to support the community, particularly researchers who are new to inclusive data experiences.

Furthermore, along with a report summarizing the meeting outcomes including the main dimensions and a research agenda, we plan to write a book on inclusive data experiences that can serve as a foundational resource for both newcomers and established researchers.

References

[1] Kim Marriott, Bongshin Lee, et al. (2021) Inclusive data visualization for people with disabilities: a call to action. Interactions, 28(3), 47–51.

[2] Bongshin Lee, Kim Marriott, et al. (2024) Inclusive Data Visualization (Dagstuhl Seminar 23252). Dagstuhl Report, 13(6), 81-105.