In this chapter we identify scientific gaps research to date regarding the ability of IAT scores to explain real world racial gaps. We use the term “IAT scores” rather than “implicit bias” because, as we show: (1) Implicit bias has no consensual scientific definition; (2) A definition offered by Greenwald (2017) is shown to be logically incoherent and empirically unjustified; (3) Exactly what the IAT measures remains unclear. Nonetheless, meta-analyses have shown that IAT scores predict discrimination to a modest extent. Alternative explanations for gaps are briefly reviewed, highlighting that IAT scores offer only one of many possible such explanations. We then present a series of heuristic models that assume that IAT scores can only explain what is left over, after accounting for other explanations of gaps. This review concludes that IAT scores probably explain a modest portion of those gaps. Even if the IAT captures implicit biases, and those implicit biases were completely eliminated, the extent to which racial gaps would be reduced is minimal. We conclude by arguing that, despite its limitations, the IAT should not be abandoned, but that, even after twenty years, much more research is needed to fully understand what the IAT measures and explains.

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.

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.

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).

The attentive public widely believes a false proposition, namely, that the race Implicit Association Test (“IAT”) measures unconscious bias within individuals that causes discriminatory behavior. We document how prominent social psychologists created this misconception and the field helped perpetuate it for years, while skeptics were portrayed as a small group of non-experts with questionable motives. When a group highly values a goal and leaders of the group reward commitment to that goal while marginalizing dissent, the group will often go too far before it realizes that it has gone too far. To avoid the sort of groupthink that produced the mismatch between what science now knows about the race IAT and what the public believes, social psychologists need to self-consciously embrace skepticism when evaluating claims consistent with their beliefs and values, and governing bodies need to put in place mechanisms that ensure that official pronouncements on policy issues, such as white papers and amicus briefs, are the product of rigorous and balanced reviews of the scientific evidence and its limitations.

Chapter 6 addresses the common strategy to appear unprejudiced: racial colorblindness. Are individuals in a color-salient society able to not see color? The chapter begins with empirical research on the question of whether people are able to ignore the race and ethnicity of others. As it turns out, people who attempt to ignore race cannot, and tend to have awkward interactions with people of color. Instead of colorblindness being a good strategy to avoid discrimination, colorblindness facilitates the ignoring of discrimination. The pros and cons of a multicultural perspective as an alternative to racial colorblindness is discussed. Implications of the cultural emphasis on colorblindness is interrogated, such as the implicit belief that white people are true Americans, whereas people of color are only provisionally American. Strategies for prejudice reduction end the chapter and include creating more complex social identities and coalition work – organizing across difference.

This chapter discusses the Implicit Association Test (IAT) and its variants as novel measures for implicit language attitudes. The IAT is a reaction time-based categorisation task that measures automatic associations between a target and attribute concept. In the case of language attitudes, this comes down to measuring whether people automatically associate a language (variety) or linguistic feature with positive/negative valence or with specific social attributes of the speaker. The method originates in social psychology, where it has been used to study a wide variety of topics (e.g. racial bias, self-esteem), and has recently been introduced to linguistics to study the social meaning of language variation. This chapter discusses the merits and potential disadvantages of using the IAT paradigm for such linguistic purposes. In addition, the chapter gives a practical introduction to setting up a study using the IAT paradigm and explains how to analyse the reaction time data harvested in the experiment and interpret the results. The main points of the chapter are illustrated with a case study that uses the P-IAT to measure associations with Standard Belgian Dutch as well as two regional varieties of Belgian Dutch.