On average, Black Americans’ health is poorer than that of White Americans. We examine three pathways by which implicit racial bias may contribute to racial health disparities. First, implicit and explicit racial bias cause racial discrimination, producing chronic stress and limited access to resources among Black targets of discrimination. This directly and negatively affects their health. This pathway has substantial empirical support. Second, physician implicit racial bias negatively affects treatment recommendations to Black patients, causing racial health disparities. Although intuitively appealing, currently there is little empirical support for this pathway. Third, physician implicit racial bias negatively influences the quality of healthcare interactions with Black patients, causing racial health disparities. This pathway has substantial empirical support. We conclude by highlighting differences in the ways social cognition and applied health disparity researchers study implicit racial bias, and make an argument for the benefits of dialogue and mutual collaborations between these two groups.

Recent findings show that it is possible in some cases to robustly and durably change implicit impressions of novel individuals. This work presents a challenge to long-standing theoretical assumptions about implicit impressions, and raises new research directions for changing and reducing implicit bias toward outgroups. Namely, implicit impressions of newly encountered individuals and groups are more amenable to robust change and updating than previously assumed, and some of the lessons from this work point to when and how we might try to change implicit bias toward well-known and familiar stigmatized groups and individuals.

There are widespread assumptions that implicit group bias leads to biased behavior. This chapter summarizes existing evidence on the link between implicit group bias and biased behavior, with an analysis of the strength of that evidence for causality. Our review leads to the conclusion that although there is substantial evidence that implicit group bias is related to biased behavior, claims about causality are not currently supported. With plausible alternative explanations for observed associations, as well as the possibility of reverse causation, scientists and policy makers need to be careful about claims made and actions taken to address discrimination, based on the assumption that implicit bias is the problem.

In this chapter, we examine the public’s understanding of implicit bias, a topic that has only recently caught the public’s attention. Given that political elites often set the contours of debate on political issues, we begin by conducting a systematic content analysis of newspaper headlines and cable news transcripts to assess the prevalence and nature of media coverage of implicit bias. We find that partisan media utilize starkly different frames regarding the scientific validity of the concept of implicit bias, the political intentions of those who use the phrase, and the requisite political recourses (if any). We then utilize two individual-level datasets to examine the mass public’s understanding of implicit bias. An original survey reveals a stark gulf in partisan understandings of implicit bias and an analysis of Project Implicit data highlights the interplay between personalized feedback from the IAT and ideology in shaping evaluations of the IAT. We conclude with a discussion of the challenges of science communication, particularly on issues relating to race, in a politically polarized age.

Despite twenty years of research, we have not yet reached a point of consensus about what might be considered the most important issue in the study of implicit bias: when and how strongly does it shape cognition and behavior? This section of this handbook reviews some of the relevant literature.

Recent decades have seen a series of attempts to further develop measures of implicit bias. Some observers have suggested drawing on lessons learned in the literature on optimal measurement of explicit bias to enhance implicit bias measures. Suggestions have also been made about how to improve meta-analyses of studies quantifying the strength of the link between implicit attitudes and behavior. For example, outdated statistical methods used in many meta-analyses of implicit bias may have led to incorrect inferences about the average effect sizes and can be avoided using newer techniques. Further improvement has been suggested to more effectively take into account omitted variables that may create spurious associations of implicit attitudes and behavior.

I highlight three issues pertaining to the Implicit Association Test (IAT). First, using the test’s documented validity estimates, I show that using the IAT to classify individuals can result in lower adherence to a benchmark of rationality than using a blatantly unfair categorization scheme. I also suggest that using base rates to classify people when negligible individuating information is available is rational. In fact, people use racial base rates when executing their own classification strategy but denigrate other people for doing so. Second, I emphasize the very tenuous relation between one’s IAT score and dependent variables such as medical therapy choices which can be influenced by multiple factors other than prejudice. Third, I question the use of the IAT as a basis for deeming a person to be implicitly racist and therefore ineligible to be hired or in need of “diversity training” whose benefits have yet to be established.

There are many reasons why the implicit bias construct took root in everyday conversation, but one of them is that millions of people have experienced the most commonly used measure of implicit bias – the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) at the Project Implicit websites. Project Implicit is a non-profit organization and international research collaboration between behavioral scientists interested in implicit social cognition. The organization’s primary public contribution is its education websites (https://implicit.harvard.edu) where more than twenty-eight million IATs have been completed. This chapter provides an overview of Project Implicit and the contributions and challenges of more than twenty years of internet-based data collection on implicit attitudes and stereotypes. The first section describes Project Implicit’s history and organizational structure; next, some of the key insights gleaned from the data collected at the Project Implicit websites are reviewed. These include assessment of the pervasiveness and correlates of implicit bias, comparisons across time and by geographic area, and reactions to learning about one’s own implicit bias. Finally, we reflect on some of the challenges of being uniquely situated between academic researchers and the general public, and describe how changing scientific knowledge has changed scientific communication about implicit bias.

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).

On the basis of dual-process theories, we propose a model that accounts for the (lack of) convergence between explicit and implicit dispositions, the effects of explicit and implicit dispositions on controlled and automatic behavior, and changes in explicit dispositions that are based on self-observed automatic behavior. The model is characterized by nine direct effects among the constructs and measures that are included the model. Importantly, in this model, each effect can be moderated by characteristics of persons, situations, and person x situation interactions. As a general theoretical framework, the model can be applied to a variety of individual difference constructs such as personality traits, values, norms, attitudes, prejudice, beliefs, stereotypes, judgment bias, and discrimination. We present evidence from our research and from studies by other researchers that speaks to the validity and usefulness of the model. We propose that implicit–explicit consistency should be considered a variable in itself and demonstrate its usefulness for understanding some findings that have been reported in the self-concept and self-esteem literatures. We make suggestions for future research with an emphasis on the discrimination of automatic and controlled behavior using facial expressions of emotion as an example.

During the past century, racial attitudes in America have been radically transformed. One hundred years ago, this was a country of explicit racism, where separation of the races and discrimination against African Americans in particular were normative, formalized in laws, in the widespread practices of businesses and in the treatment of individuals by individuals every day. The civil rights movement of the 1960s brought about a landmark shift, eliciting widespread condemnation of racism, and setting the stage for the country’s embracing of multiculturalism and implementing policies in many arenas of life to level the playing field and compensate for past discrimination. These changes in public practices were accompanied by a gradual transformation of public opinion in the United States: surveys documented a steady growth of endorsement of racial equality and a decline in explicitly stated racial prejudice. More and more Americans endorsed principles of racial equality and expressed support for various policies preventing discrimination.