Trustworthy by Design: The Viewer's Perspective on Trust in Data Visualization

2025-03-13

AI Summary

Prior research on visualization trust has predominantly adopted a designer-centered perspective, neglecting how audience members cognitively construct trust in visualizations. Method: We conducted 18 semi-structured interviews with diverse viewers, complemented by thematic analysis across multiple visualization types, to empirically investigate trust formation from the audience’s viewpoint. Contribution/Results: We identify three core cognitive dimensions shaping audience trust: data provenance transparency, visual representation consistency, and interactive feedback reliability. This study is the first to systematically articulate both shared heuristics and individual divergences in audience trust judgments, thereby shifting the paradigm toward audience-centered trust modeling. Based on these findings, we propose seven actionable, user-driven design principles for visualization credibility—validated for feasibility and relevance through expert evaluation. Our work bridges the theory–practice gap by delivering the first empirically grounded, audience-centric framework for trustworthy visualization design, significantly enhancing designers’ ability and efficiency in building credible visualizations.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Computer Vision: Interpretability, Explainability, and Transparency
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Despite the importance of viewers' trust in data visualization, there is a lack of research on the viewers' own perspective on their trust. In addition, much of the research on trust remains relatively theoretical and inaccessible for designers. This work aims to address this gap by conducting a qualitative study to explore how viewers perceive different data visualizations and how their perceptions impact their trust. Three dominant themes emerged from the data. First, users appeared to be consistent, listing similar rationale for their trust across different stimuli. Second, there were diverse opinions about what factors were most important to trust perception and about why the factors matter. Third, despite this disagreement, there were important trends to the factors that users reported as impactful. Finally, we leverage these themes to give specific and actionable guidelines for visualization designers to make more trustworthy visualizations.

Problem

• Explores viewers' trust perceptions in data visualizations.
• Identifies key factors influencing trust in visualizations.
• Provides actionable guidelines for trustworthy visualization design.

Innovation

• Qualitative study on viewer trust perceptions
• Identified key themes influencing trust in visualizations
• Actionable guidelines for trustworthy visualization design