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.
翻译:可视化研究往往侧重于感知准确性,或帮助读者解释关键信息。然而,我们很少知道图表设计会如何影响读者对数据背后的人的看法。具体地说,设计可以与读者的社会认知偏见互动,从而延续有害的陈规定型观念。例如,分析社会不平等时,条形图是一种流行的选择,可以显示种族、性别或其他群体之间结果的差异。但是,条形图可能会鼓励赤字思维,认为结果差异是由群体的个人长处或短处而不是外部因素造成的。这些错误的个人属性可以强化关于被视觉化的群体的各种定型观念。我们进行了四次实验,研究影响归属偏差(因而是赤字思维)的设计选择。众工们观看了可视化社会结果,这些社会结果要么掩盖了数据的变异性,例如条形图或圆点图,要么强调数据的变异性,例如小幅图或预测间隔。他们报告说,他们同意对可视化差异的个人和外部解释都有相同的看法。总体而言,当参与者看到隐藏群体内部变异性的可视化,他们就会更赞同个人解释。当他们看到强调群体内部变异性时,他们在强调群体内变异性时,他们会强调群体内变性,他们会倾向于个人变性,而会增加个人变性。这些定型的变性。他们会倾向于个人变性。