A primary goal of prejudice and stereotyping research is to reduce intergroup disparities arising from various forms of bias. For the last thirty years, much, perhaps most, of this research has focused on implicit bias as the crucial construct of interest. There has been, however, considerable confusion and debate about what this construct is, how to measure it, whether it predicts behavior, how much it contributes to intergroup disparities, and what would signify successful intervention against it. We argue that this confusion arises in part because much work in this area has focused narrowly on the automatic processes of implicit bias without sufficient attention to other relevant psychological constructs and processes, such as people’s values, goals, knowledge, and self-regulation (Devine, 1989). We believe that basic research on implicit bias itself is important and can contribute to reducing intergroup disparities, but those potential contributions diminish if and when the research disregards controlled processes and the personal dilemma faced by sincerely nonprejudiced people who express bias unintentionally. We advocate a renewed focus on this personal dilemma as an important avenue for progress.

The concept of unconscious bias is firmly entrenched in American society, yet evidence has accumulated in recent years questioning widely accepted claims about the phenomenon, including assertions that it can be measured reliably, influences behavior and is susceptible to intervention. We adopt a two-pronged approach to investigating the state of affairs: First, assessing claims made about unconscious bias in the public sphere; and second, conducting a national public opinion survey – the first of its kind, to the extent we can ascertain – designed to measure public understanding of unconscious bias. Results show that broad majorities of Americans think unconscious biases are prevalent, influence behavior and can be mitigated through training. Confidence in its accurate measurement is lower. The public sees unconscious biases as more prevalent than biases that are consciously held, and as worthy of mitigation efforts by businesses and government. Our chapter assesses these attitudes and understandings and compares them with the state of the science on unconscious bias.

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.

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.

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.

Scholars have long recognized that successful prediction of behavior on the basis of explicit attitudes depends on the correspondence between the attitude measure and the focal behavior. Fishbein and Ajzen (2010) argued that behaviors vary in terms of their action, target, context, and time, and that the prediction of specific behaviors is greatly enhanced when explicit attitude measures reflect these features of the to-be-predicted behavior. We argue that the same principle applies in the case of predicting behavior from implicit attitudes, and we review relevant evidence relating to each of Fishbein and Ajzen’s parameters. Special attention is paid to the target parameter, given increasing awareness of the intersectional nature of bias. A global race bias may not extend equally to all members of a particular racial identity, and cross-cutting factors such as gender, age, or sexuality may qualify the extent to which global measures of race bias predict discriminatory behavior toward particular individuals.

The last two decades have been marked by excitement for measuring implicit attitudes and implicit biases, as well as optimism that new technologies have made this possible.  Despite considerable attention, this movement is marked by weak measures.  Current implicit measures do not have the psychometric properties needed to meet the standards required for psychological assessment or necessary for reliable criterion prediction. Some of the creativity that defines this approach has also introduced measures with unusual properties that constrain their applications and limit interpretations.  We illustrate these problems by summarizing our research using the Implicit Association Test (IAT) as a case study to reveal the challenges these measures face. We consider such issues as reliability, validity, model misspecification, sources of both random and systematic method variance, as well as unusual and arbitrary properties of the IAT’s metric and scoring algorithm. We then review and critique four new interpretations of the IAT that have been advanced to defend the measure and its properties. We conclude that the IAT is not a viable measure of individual differences in biases or attitudes. Efforts to prove otherwise have diverted resources and attention, limiting progress in the scientific study of racism and bias.

This chapter reviews research on a contemporary form of prejudice – aversive racism – and considers the important role of implicit bias in the subtle expressions of discrimination associated with aversive racism. Aversive racism characterizes the racial attitudes of a substantial portion of well-intentioned people who genuinely endorse egalitarian values and believe that they are not prejudiced but at the same time possess automatically activated, often nonconscious, negative feelings and beliefs about members of another group. Our focus in this chapter is on the bias of White Americans toward Black Americans, but we also discuss relevant findings in other intergroup contexts. We emphasize the importance of considering, jointly, both explicit and implicit biases for understanding subtle, and potentially unintentional, expressions of discrimination. The chapter concludes by discussing how research on aversive racism and implicit bias has been mutually informative and suggests specific promising directions for future work.

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.

The nature of prejudice and bigotry have changed in recent decades. In most communities it is unacceptable to be openly racist, sexist, or homophobic. Norms against prejudice have certainly changed. It is true that prejudice directed toward many groups has decreased; however, individual attitudes have not necessarily caught up with changing norms. As a result, some people hide their prejudices, attempting to mask their discrimination in neutral-seeming behavior. Others truly believe they are not prejudiced, even when they are. Social psychologists have spent recent decades measuring and mapping the nature of subtle, covert, and implicit forms of contemporary prejudice. Benign Bigotry critically examines seven contemporary myths and assumptions that reflect prejudice that appears common sense, even harmless, but actually reveal the perniciousness and insidiousness of contemporary prejudice. Benign Bigotry critically analyzes: (1) the assumption that prejudice is an individual-only problem; (2) that people in outgroups are all alike; (3) that those accused of a crime are likely guilty of something; (4) that feminists are manhaters; (5) that LGBTQ+ people flaunt their sexuality; (6) that those who claim racial colorblindness are not racists; and (7) that affirmative action amounts to reverse racism.

Chapter 4 provides original data on the way sexual assault was adjudicated across the country in the wake of the Dear Colleague Letter. The chapter presents data gathered from eighty-five of the top colleges and universities over a twenty-seven-month period, from October 2014 to January 2017. It asks about rights deemed fundamental in a criminal trial including: the right to a live hearing, the right to question the opposing party, the right to appeal, and the right to remain silent.