The Racialized Basis of Trait Judgments from Faces

人们通常通过面部表情来判断他人。面部线索被用来判断他人的情绪表达（如快乐或愤怒），并推断他人的性格特征（如可信度或能力）。这些判决很重要，因为它们影响到就业、医疗保健或刑事审判等领域的决定。面部线索也被其他人用来推断一个人的种族。这个项目的重点是，感知者有时可能会使用相同的面部线索来推断一个人的特征和种族。因此，根据面部特征来评估另一个人的特征的感知者实际上可能是根据种族差异来评估这个人的。提出的种族-特征重叠模型表明，相同的面部特征被用来推断种族和特征，这有助于解释从面部推断特征时偏见的原因和后果。这项研究的最终目的是更好地理解种族偏见是如何在人们的头脑中发挥作用的，并找到减少这种偏见的方法。这个研究项目调查人们如何利用种族化的面部线索（如肤色）来推断他人的特征。一组实验试图为种族-特质重叠假说建立证据。另一项研究表明，这些影响受到个体差异和群体差异的限制。第三组研究了如何通过将感知者对特征的表征与他们对种族的表征分离来减少这些重叠效应。种族-性状重叠模型产生了多种新颖且可测试的预测。例如，该模型表明，这些影响存在于感知者的头脑中，这意味着改变感知者的心理表征应该改变重叠。该项目提供了几种方法上的进步，并对面部分析在商业和工业中的应用具有重要意义。该模型有助于解释利用面部线索的机器学习算法中出现的社会偏见证据，并指出减少这种偏见的方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

People commonly make inferences about others based on their facial expressions. Facial cues are used to gauge others’ emotional expressions (such as happiness or anger) and to make inferences about others’ traits (such as trustworthiness or competence). These judgments are important because they affect decisions in such areas as employment, healthcare or criminal trials. Facial cues are also used by others to make inferences about a person’s race. This project focuses on the idea that perceivers may sometimes use the very same facial cues to draw inferences both about a person’s traits and about their race. As a result, perceivers who are evaluating another person’s traits based on facial cues may actually be evaluating that person based on variations in race. The proposed Race-Trait Overlap model suggests that the same facial features are used to infer race and traits, which helps to account for causes and consequences of biases in trait inferences from faces. The ultimate aim of the research is to better understand how racial prejudice works in the mind, and to identify ways of reducing such biases.This program of research investigates how people may employ racialized facial cues (such as skin tone) to infer others’ traits. One set of experiments seeks to establish evidence for the Race-Trait Overlap hypothesis. Another demonstrates that these effects are qualified both by individual differences and group differences. A third set investigates how these overlap effects can be reduced by decoupling perceivers’ representations of traits from their representations of race. The Race-Trait Overlap model yields multiple novel and testable predictions. For example, the model suggests that these effects exist in the minds of perceivers, which means that changing perceivers’ mental representations should change the overlap. The project offers several methodological advances, and carries important implications for how facial analytics are used in business and industry. The model helps to account for emerging evidence of social bias in machine learning algorithms that utilize facial cues, and points the way to reducing such bias.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.