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

TL;DR

This study explores how visualization practitioners perceive and navigate their personal beliefs, biases, and societal influences when designing visualizations of race and gender data, highlighting complex challenges and considerations.

Contribution

It provides empirical insights into practitioners' awareness of social framing in visualization design, an area previously underexplored.

Findings

Practitioners' personal experiences influence their design choices.

Values and biases impact how race and gender data are visualized.

Challenges include balancing neutrality with social responsibility.

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 and practice in this space.