We offer a conceptual framework by which to consider implicit bias. In contrast to a far too common presumption that implicit bias involves unconscious attitudes and stereotypes, i.e., ones for which individuals lack awareness, we emphasize a view of implicit bias as an effect of attitudes of which individuals are unaware. The perspective is grounded in decades of social psychological theory and research concerning the constructive nature of perception and the potential biasing influence of attitudes on perceptions and judgments. Attitudes that are automatically activated from memory can exert such a biasing influence, without individuals’ awareness that they have been affected. We articulate the advantages of such a perspective for both the science and the politics of implicit bias. We also discuss how individuals can overcome the influence of an automatically activated attitude, given appropriate motivation and opportunity to do so, and briefly review evidence concerning the joint influence of these factors on prejudicial judgments and behavior.

Implicit bias has always been understood as an individual attitude that is rooted in one’s social environment. However, in practice, the field has focused more heavily on the individual attitude, to the neglect of the social environment. In this chapter, we describe an alternative view of implicit bias – the Bias of Crowds model – that reinterprets implicit bias as a feature of social contexts more than persons. In doing so, we argue that, akin to the “wisdom of crowds” effect, implicit bias may emerge as the aggregate effect of individual fluctuations in concept accessibility that are transitory and context-dependent. We also explain how this novel interpretation of implicit bias resolves long-standing concerns regarding the temporal instability and weak predictive validity of implicit attitudes measures. Finally, we review direct empirical tests of the model and its predictions and consider future avenues for research, as well as theoretical and practical implications.

A recent debate on implicit measures of racial attitudes has focused on the relative roles of the person, the situation, and their interaction in determining the measurement outcomes. The chapter describes process models for assessing the roles of the situation and the person-situation interaction on the one hand and stable person-related components on the other hand in implicit measures. Latent state-trait models allow one to assess to what extent the measure is a reliable measure of the person and/or the situation and the person-situation interaction (Steyer, Geiser, & Fiege, 2012). Moreover, trait factor scores as well as situation-specific residual factor scores can be computed and related to third variables, thereby allowing one to assess to what extent the implicit measure is a valid measure of the person and/or the situation and the person-situation interaction. These methods are particularly helpful when combined with a process decomposition of implicit-measure data such as a diffusion-model analysis of the IAT (Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007).

Implicit measures were introduced to explain phenomena that are characterized by a gap between self-reported attitudes and behavior. Recent meta-analyses revealed, however, that implicit measures have only limited predictive validity that goes beyond self-reports. We identify possible reasons for this failure: (a) A lack of validity that is due to the influence of extraneous processes, (b) a focus on evaluation instead of motivation, (c) a focus on associations instead of propositional beliefs, and (d) a focus on global instead of context-dependent attitudes. Recent developments in the field of implicit measures addressed these problems: (a) Statistical process models increase the internal validity of implicit measures, (b) implicit measures of wanting have the potential to predict behavior better than implicit measures of liking, (c) new paradigms provide measures of automatically activated attitudes for propositions that have an unambiguous interpretation, and (d) assessment of context-dependent beliefs is better suited to predict specific behaviors. Incorporating these developments into research on implicit bias will help to realize the initial expectations of describing, explaining, and predicting behavior in many situations.

Various areas in psychology are interested in whether specific processes underlying judgments and behavior operate in an automatic or nonautomatic fashion. In social psychology, valuable insights can be gained from evidence on whether and how judgments and behavior under suboptimal processing conditions differ from judgments and behavior under optimal processing conditions. In personality psychology, valuable insights can be gained from individual differences in behavioral tendencies under optimal and suboptimal processing conditions. The current chapter provides a method-focused overview of different features of automaticity (e.g., unintentionality, efficiency, uncontrollability, unconsciousness), how these features can be studied empirically, and pragmatic issues in research on automaticity. Expanding on this overview, the chapter describes the procedures of extant implicit measures and the value of implicit measures for studying automatic processes in judgments and behavior. The chapter concludes with a discussion of pragmatic issues in research using implicit measures.