  Visualization research often focuses on perceptual accuracy or helping
readers interpret key messages. However, we know very little about how chart
designs might influence readers' perceptions of the people behind the data.
Specifically, could designs interact with readers' social cognitive biases in
ways that perpetuate harmful stereotypes? For example, when analyzing social
inequality, bar charts are a popular choice to present outcome disparities
between race, gender, or other groups. But bar charts may encourage deficit
thinking, the perception that outcome disparities are caused by groups'
personal strengths or deficiencies, rather than external factors. These faulty
personal attributions can then reinforce stereotypes about the groups being
visualized. We conducted four experiments examining design choices that
influence attribution biases (and therefore deficit thinking). Crowdworkers
viewed visualizations depicting social outcomes that either mask variability in
data, such as bar charts or dot plots, or emphasize variability in data, such
as jitter plots or prediction intervals. They reported their agreement with
both personal and external explanations for the visualized disparities.
Overall, when participants saw visualizations that hide within-group
variability, they agreed more with personal explanations. When they saw
visualizations that emphasize within-group variability, they agreed less with
personal explanations. These results demonstrate that data visualizations about
social inequity can be misinterpreted in harmful ways and lead to stereotyping.
Design choices can influence these biases: Hiding variability tends to increase
stereotyping while emphasizing variability reduces it.

Human-Computer Interaction;Computers and Society