Abstract
Effective visualizations on population data can help reveal societal and systematic problems and even offer insights to possible solutions. Despite all of the utilitarian benefits that conventional forms of visualizations can provide, there have been attempts to offer new modes of representing data by shaping graphs into the likeness of humans. While the motivations and approaches to these experimental and anthropomorphic forms of data-driven graphics may be novel, some of the results of these visualizations have yielded problematic portrayals and surveillance of BIPOC groups. What these shortcomings show is the implicit bias and the shortsightedness in the history and field of data visualization that can serve as important lessons to its practitioners. They can teach us that any attempt to construct new forms of visualizations—particularly those that resemble human bodies—must be sensitive and inclusive to all peoples through thoughtful consideration of their parameters and user testing with diverse groups. This study puts forward the shortcomings of two data-driven graphics, ISOTYPE and Chernoff Faces as case studies. The intention behind this position is not to suggest that efforts to anthropomorphize data is a self-defeating ambition, but to help promote inclusive design practices for practitioners of the field.
Presenters
Eugene ParkAssociate Professor, College of Design, University of Minnesota, Twin Cities, Minnesota, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Data Visualization, Information Design, ISOTYPE, Chernoff Faces
