A Critical Reflection on the Values and Assumptions in Data Visualization

2026-02-25

AI Summary

This study critically examines the dominant values underpinning mainstream data visualization—namely universality, objectivity, and efficiency—and argues that these principles have historically marginalized diverse perspectives and cultural differences. Through a systematic review of foundational theories by Bertin, Tukey, Wilkinson, Ware, and Munzner, the work employs literary analysis and critical theory to trace and deconstruct the implicit value assumptions embedded in canonical frameworks. Drawing on interdisciplinary insights, the research exposes limitations in current paradigms as manifested in visualization tool design, pedagogy, and scholarly practice. It further proposes a novel pathway toward value pluralism, advocating for a more inclusive and reflexive approach to inform the development of future visualization systems, design guidelines, and research agendas.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Other Foundations of Computer Vision
• Knowledge Representation and Reasoning: Other Foundations of Knowledge Representation & Reasoning

Application Category

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

Abstract

Visualization has matured into an established research field, producing widely adopted tools, design frameworks, and empirical foundations. As the field has grown, ideas from outside computer science have increasingly entered visualization discourse, questioning the fundamental values and assumptions on which visualization research stands. In this short position paper, we examine a set of values that we see underlying the seminal works of Jacques Bertin, John Tukey, Leland Wilkinson, Colin Ware, and Tamara Munzner. We articulate three prominent values in these texts - universality, objectivity, and efficiency - and examine how these values permeate visualization tools, curricula, and research practices. We situate these values within a broader set of critiques that call for more diverse priorities and viewpoints. By articulating these tensions, we call for our community to embrace a more pluralistic range of values to shape our future visualization tools and guidelines.

Problem

• data visualization
• values
• assumptions
• objectivity
• pluralism

Innovation

• critical reflection
• values in visualization
• pluralism
• data visualization ethics
• epistemological assumptions