The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender Demographic Data

2025-02-13

AI Summary

This study investigates how data visualization practitioners recognize and address the influence of their own values, biases, and positional power when representing race- and gender-related demographic data. Drawing on in-depth interviews with 17 practitioners and employing thematic and critical discourse analysis, the research systematically documents— for the first time—practitioners’ reflexive awareness of data’s social construction and the practical tensions this awareness entails. Key challenges identified include ambiguous ethical decision-making, the illusion of neutrality, contested data agency, and unclear audience accountability. The study introduces the novel “reflexive fairness design” framework, comprising six actionable design principles and pedagogical recommendations. This framework advances the field from a technocentric, purportedly neutral stance toward a critically engaged, responsibility-oriented practice paradigm. (138 words)

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Data visualizations are increasingly seen as socially constructed, with several recent studies positing that perceptions and interpretations of visualization artifacts are shaped through complex sets of interactions between members of a community. However, most of these works have focused on audiences and researchers, and little is known about if and how practitioners account for the socially constructed framing of data visualization. In this paper, we study and analyze how visualization practitioners understand the influence of their beliefs, values, and biases in their design processes and the challenges they experience. In 17 semi-structured interviews with designers working with race and gender demographic data, we find that a complex mix of factors interact to inform how practitioners approach their design process, including their personal experiences, values, and their understandings of power, neutrality, and politics. Based on our findings, we suggest a series of implications for research, design, and education in this space.

Problem

• Social construction of visualizations
• Practitioner challenges in design
• Race and gender data visualization

Innovation

• Social construction in data visualizations
• Practitioner beliefs and biases analysis
• Semi-structured interviews with designers