分散与差异：隐藏可变性会在可视化社会结果时鼓励刻板印象
Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes
摘要：
可视化研究通常侧重于感知准确性或帮助读者解释关键信息。然而，我们对图表设计如何影响读者对数据背后的人的看法知之甚少。具体来说，设计是否会与读者的社会认知偏见相互作用，从而使有害的刻板印象永久化？例如，在分析社会不平等时，条形图是一种流行的选择，用于呈现种族，性别或其他群体之间的结果差异。但条形图可能会鼓励赤字思维，即认为结果差异是由群体的个人优势或缺陷造成的，而不是外部因素。这些错误的个人属性可能会强化人们对被形象化的群体的刻板印象。我们进行了四个实验，研究影响归因偏差（因此是赤字思维）的设计选择。众包工作者查看了描绘社会结果的可视化，这些可视化要么掩盖数据的可变性，如条形图或点图，要么强调数据的可变性，如抖动图或预测区间。他们报告说，他们同意个人和外部的解释可视化的差距。总体而言，当参与者看到隐藏组内差异的可视化时，他们更同意个人解释。当他们看到强调组内差异的可视化时，他们不太同意个人解释。这些结果表明，关于社会不平等的数据可视化可能会以有害的方式被误解，并导致刻板印象。设计选择可以影响这些偏见：隐藏可变性往往会增加刻板印象，而强调可变性会减少它。
英文摘要：
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.
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