The Racialized Basis of Trait Judgments from Faces

从面孔判断特质的种族化基础

• 批准号: 2215153
• 负责人: Kurt Hugenberg
• 金额: $ 49.98万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215153&HistoricalAwards=false
• 关键词: Racialized; Basis; Trait; Judgments; Faces

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.

人们通常会根据他人的面部表情来推断他人。面部提示用于评估他人的情感表达（例如幸福或愤怒），并对他人的特征（例如可信度或能力）进行推断。这些法官很重要，因为它们会影响就业，医疗保健或刑事审判等领域的决策。他人还使用面部提示来推断一个人的种族。该项目的重点是玩家有时可能会使用相同的面部提示来提出有关一个人的特征和种族的推论。结果，根据面部提示评估他人特征的人员实际上可能是根据种族的变化来评估该人的。拟议的种族特征重叠模型表明，相同的面部特征用于推断种族和特征，这有助于解释偏见的原因和后果。这项研究的最终目的是更好地了解种族偏见如何在思想中起作用，并确定减少这种偏见的方式。该研究计划研究人们如何使用种族化的面部提示（例如肤色）来推断他人的特征。一组实验试图为种族特征重叠假设建立证据。另一个表明，这些效应既由个体差异和群体差异来限定。第三组调查了如何通过将感知者的表现与种族的表现方式脱在一起，如何减少这些重叠效果。种族特征重叠模型产生了多个新颖和可测试的预测。例如，该模型表明这些效果存在于感知者的思想中，这意味着改变感知者的心理表征应改变重叠。该项目提供了几种方法论的进步，并对面部分析在商业和行业中的使用方式具有重要意义。该模型有助于说明使用面部提示的机器学习算法中社会偏见的出现，并为减少这种BIA的方式指出了这一奖项。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估来获得的支持。