Designing for Disclosure in Data Visualizations

2025-08-11

AI Summary

Data transformation operations in visualization can unintentionally or deliberately conceal or reveal information, yet existing tools lack systematic guidance on information disclosure. Method: This study introduces the novel concept of “disclosure strategies” and establishes the first taxonomy of disclosure tactics specifically for visualization, explicitly integrating information disclosure as a core design dimension. Through content analysis of 425 academic visualization examples, we derive a classification framework spanning data aggregation, filtering, visual encoding, and interaction. We validate its usability via iterative design practice. Contribution/Results: The taxonomy provides a theoretical foundation and methodological support for visualization tool development, ethical design standards, and user information literacy training. It significantly advances understanding of information exposure mechanisms in visualization, enabling designers to make intentional, transparent, and ethically grounded decisions about what—and how much—information is disclosed through visual representations.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Visualizing data often entails data transformations that can reveal and hide information, operations we dub disclosure tactics. Whether designers hide information intentionally or as an implicit consequence of other design choices, tools and frameworks for visualization offer little explicit guidance on disclosure. To systematically characterize how visualizations can limit access to an underlying dataset, we contribute a content analysis of 425 examples of visualization techniques sampled from academic papers in the visualization literature, resulting in a taxonomy of disclosure tactics. Our taxonomy organizes disclosure tactics based on how they change the data representation underlying a chart, providing a systematic way to reason about design trade-offs in terms of what information is revealed, distorted, or hidden. We demonstrate the benefits of using our taxonomy by showing how it can guide reasoning in design scenarios where disclosure is a first-order consideration. Adopting disclosure as a framework for visualization research offers new perspective on authoring tools, literacy, uncertainty communication, personalization, and ethical design.

Problem

• Analyzing how data visualizations reveal or hide information
• Developing a taxonomy for disclosure tactics in visualization design
• Providing guidance for ethical and effective visualization authoring

Innovation

• Content analysis of 425 visualization examples
• Taxonomy organizing data representation changes
• Framework for design trade-offs in disclosure