The Racialized Basis of Trait Judgments from Faces

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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.