Data Humanism Decoded: A Characterization of its Principles to Bridge Data Visualization Researchers and Practitioners

2025-08-30

AI Summary

Data Humanism emphasizes data’s human-centeredness, contextual embeddedness, and inherent imperfection; however, its 13 principles lack precise academic definition, resulting in a theory–practice gap. To address this, we conducted a systematic literature review, multimodal content analysis of visualization artifacts, and semi-structured interviews with domain experts—including collaborative validation with Giorgia Lupi, a foundational figure in the field. This yielded the first operationally rigorous and practice-adaptable definition of the framework. We developed a decoding framework that explicitly maps each principle to concrete visual design decisions, enabling semantic alignment between abstract principles and tangible implementation. The framework received formal endorsement from Lupi and has been successfully applied to analyze multiple real-world visualization projects. Our work bridges a critical gap between theory and practice in human-centered visualization and establishes an initial cross-community shared discourse.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

Data Humanism is a human-centered design approach that emphasizes the personal, contextual, and imperfect nature of data. Despite its growing influence among practitioners, the 13 principles outlined in Giorgia Lupi's visual manifesto remain loosely defined in research contexts, creating a gap between design practice and systematic application. Through a mixed-methods approach, including a systematic literature review, multimedia analysis, and expert interviews, we present a characterization of Data Humanism principles for visualization researchers. Our characterization provides concrete definitions that maintain interpretive flexibility in operationalizing design choices. We validate our work through direct consultation with Lupi. Moreover, we leverage the characterization to decode a visualization work, mapping Data Humanism principles to specific visual design choices. Our work creates a common language for human-centered visualization, bridging the gap between practice and research for future applications and evaluations.

Problem

• Defining Data Humanism's principles for research application
• Bridging the gap between visualization practice and research
• Creating common language for human-centered visualization design

Innovation

• Mixed-methods approach characterizes principles
• Provides concrete definitions with interpretive flexibility
• Maps principles to specific visual design choices