The Bias of Crowds model (Payne, Vuletich, & Lundberg, 2017) argues that implicit bias varies across individuals and across contexts. It is unreliable and weakly associated with behavior at the individual level. But when aggregated to measure context-level effects, the scores become stable and predictive of group-level outcomes. We concluded that the statistical benefits of aggregation are so powerful that researchers should reconceptualize implicit bias as a feature of contexts, and ask new questions about how implicit biases relate to systemic racism. Connor and Evers (2020) critiqued the model, but their critique simply restates the core claims of the model. They agreed that implicit bias varies across individuals and across contexts; that it is unreliable and weakly associated with behavior at the individual level; and that aggregating scores to measure context-level effects makes them more stable and predictive of group-level outcomes. Connor and Evers concluded that implicit bias should be considered to really be noisily measured individual construct because the effects of aggregation are merely statistical. We respond to their specific arguments and then discuss what it means to really be a feature of persons versus situations, and multilevel measurement and theory in psychological science more broadly.
