SBP: Disentangling Implicit and Explicit Bias from Responses to Exemplars and Categories

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Well-documented biases in hiring and admissions decisions often prevent qualified individuals from participating in the workforce and in education. Many current attempts to address this problem focus on reducing “implicit” or “unconscious” bias. At the same time, scientists are increasingly debating and critiquing implicit bias as a concept. While awareness of implicit biases has become commonplace, the associated science does not yet provide a solid foundation on which to build intervention efforts. The central goal of this project is to strengthen the scientific understanding of implicit bias by identifying differences in how people respond to groups and group labels, versus specific individuals and individual targets. It will reveal whether researchers and organizations wishing to address bias should be focusing on “unconscious” bias, or whether more effective interventions could be designed by focusing on bias toward groups versus individuals. The project will help address popular misconceptions of implicit bias from our theoretical understanding of how bias operates. In addition, it will generate valuable insights for addressing bias in real-world settings.This project involves 12 experiments that will tackle a key confound that obstructs the ability to draw strong conclusions about the research on implicit bias: the confound between type of measure (i.e., explicit vs. implicit) and stimuli type (i.e., categories vs. exemplars). Traditionally, research examining explicit bias typically requires participants to respond to abstract social categories (e.g., the categories Black people and White people), whereas implicit measures typically involve specific exemplars of those categories (e.g., faces of Black and White individuals). The current project uses computer-based reaction time tasks and behavioral measures to probe core themes regarding implicit-explicit relations. For example, do low correlations between common implicit and explicit measures of bias reflect (a) differences in processing awareness or do those low correlations derive from (b) the differences in stimuli type? Moreover, do category-focused evaluations and exemplar-focused evaluations have distinct consequences? By identifying the distinction between explicit and implicit measures on the one hand, and the distinction between categories and exemplars on the other, we can gain new insights into the causes and consequences of bias, with important implications for both theory, practice, and discrimination intervention programs.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.