Controlling the influence of stereotypes on one’s thoughts (Preprint title: Controlling implicit bias: Insights from a public health perspective)

Research on reducing or controlling implicit bias has been characterized by a tension between the two goals of reducing lingering intergroup disparities and gaining insight into human cognition. The tension between these two goals has created two distinct research traditions, each of which is characterized by different research questions, methods, and ultimate goals. We argue that the divisions between these research traditions are more apparent than real and that the two research traditions could be synergistic. We attempt to integrate the two traditions by arguing that implicit bias, and the disparities it is presumed to cause, is a public health problem. Based on this perspective, we identify shortcomings in our current knowledge of controlling implicit bias and provide a set of recommendations for future research

The motivation to express prejudice

Contemporary prejudice research focuses primarily on people who are motivated to respond without prejudice and the ways in which unintentional bias can cause these people to act inconsistent with this motivation. However, some real-world phenomena (e.g., hate speech, hate crimes) and experimental findings (e.g., Plant & Devine, 2001; 2009) suggest that some expressions of prejudice are intentional. These phenomena and findings are difficult to explain solely from the motivations to respond without prejudice. We argue that some people are motivated to express prejudice, and we develop the motivation to express prejudice (MP) scale to measure this motivation. In seven studies involving more than 6,000 participants, we demonstrate that, across scale versions targeted at Black people and gay men, the MP scale has good reliability and convergent, discriminant, and predictive validity. In normative climates that prohibit prejudice, the internal and external motivations to express prejudice are functionally non-independent, but they become more independent when normative climates permit more prejudice toward a target group. People high in the motivation to express prejudice are relatively likely to resist pressure to support programs promoting intergroup contact and vote for political candidates who support oppressive policies. The motivation to express prejudice predicted these outcomes even when controlling for attitudes and the motivations to respond without prejudice. This work encourages contemporary prejudice researchers to broaden the range of samples, target groups, and phenomena that they study, and more generally to consider the intentional aspects of negative intergroup behavior

Breaking the prejudice habit: Automaticity and control in the context of a long-term goal

In what follows, we will describe the problem that motivated the development of the prejudice habit model, namely, that even people who report beliefs and attitudes that are opposed to prejudice can act in discriminatory ways. We will then review the prejudice habit model and how the model uses the distinction between controlled and automatic processes to understand lingering group disparities. We will end our discussion with a review of topics for further research and the implications of the prejudice habit model for other dual process theories

The role of intentions in conceptions of prejudice: An historical perspective

In this chapter, we will draw on previous treatments of the history of prejudice research (Devine, 1995; Duckitt, 1992; Milner, 1983) to conduct an historical review of how events in both the real world and the research world have shaped researchers’ conceptualizations of the intentionality of prejudice. We argue that, while early research focused on the intentional aspects of prejudice, modern research focuses more exclusively on the unintentional aspects of prejudice. As a result of the modern overattention to the unintentional aspects of prejudice, researchers have ignored the possibility that some people are motivated to express prejudice (Forscher & Devine, in preparation). Through our argument, we hope to build a bridge between modern and classic insights into prejudice processes

Knowledge-based interventions are more likely to reduce legal disparities than are implicit bias interventions

We should note at the outset that this chapter is different from most others in this volume. Neither author is an expert of the law, legal proceedings, or the criminal justice system more generally. Instead, we are both psychological scientists who specialize in race and unintentional forms of bias. Our goal in this chapter is to review the extant work on implicit bias and interventions to change implicit bias. Though the work in this area is ever burgeoning, the evidence regarding the effectiveness of implicit bias interventions is rather mixed and the goals for the specific research efforts are quite varied. Our hope is to lay out the issues in ways that elucidate what this work can and cannot say about the causes of and remedies to disparities in legal outcomes. We hope that this review will prove helpful to those who choose to investigate the impact of biases, both unintentional and intentional, throughout the legal system. To wit, in this chapter, we will review the evidence that, in general, is required to establish that implicit bias causes legal disparities. A great deal of the evidence regarding implicit bias and legal disparities is correlational and, of course, evidence of correlations between measured implicit bias and biased behavior is insufficient to establish cause. We will then conduct a selective review of implicit bias intervention research and argue that this research also does not speak directly to whether interventions to change implicit bias are effective ways to resolve social disparities. Indeed, we will argue that, based on the extant evidence, any intervention that is focused on directly changing scores on measures of implicit bias is unlikely to be effective at changing social disparities. Finally, we will review an approach that we believe is more effective: change people’s knowledge about how the structure of the social environment makes them complicit in the perpetuation of bias. We will illustrate this approach with an intervention that we have developed, the prejudice habit-breaking intervention

Breaking the prejudice habit: Mechanisms, timecourse, and longevity

The prejudice habit-breaking intervention (Devine et al., 2012) and its offshoots (e.g., Carnes et al., 2012) have shown promise in effecting long-term change in key outcomes related to intergroup bias, including increases in awareness, concern about discrimination, and, in one study, long-term decreases in implicit bias. This intervention is based on the premise that unintentional bias is like a habit that can be broken with sufficient motivation, awareness, and effort. We conducted replication of the original habit-breaking intervention experiment in a sample more than three times the size of the original (N = 292). We also measured all outcomes every other day for 14 days and measured potential mechanisms for the intervention’s effects. Consistent with previous results, the habit-breaking intervention produced a change in concern that endured two weeks post-intervention. These effects were associated with increased sensitivity to the biases of others and an increased tendency to label biases as wrong. Contrasting with the original work, both control and intervention participants decreased in implicit bias, and the effects of the habit-breaking intervention on awareness declined in the second week of the study. In a subsample recruited two years later, intervention participants were more likely than control participants to object on a public online forum to an essay endorsing racial stereotyping. Our results suggest that the habit-breaking intervention produces enduring changes in peoples’ knowledge of and beliefs about race-related issues, and we argue that these changes are even more important for promoting long-term behavioral change than are changes in implicit bias

Little race or gender bias in an experiment of initial review of NIH R01 grant proposals

Many granting agencies allow reviewers to know the identity of a proposal’s Principal Investigator (PI), which opens the possibility that reviewers discriminate on the basis of PI race and gender. We investigated this experimentally with 48 NIH R01 grant proposals, representing a broad spectrum of NIH-funded science. We modified PI names to create separate White male, White female, Black male, and Black female versions of each proposal, and 412 scientists each submitted initial reviews for three proposals. We find little to no race or gender bias in initial R01 evaluations, and additionally find that any bias that might have been present must be negligible in size. This conclusion was robust to a wide array of statistical model specifications. Pragmatically important bias may be present in other aspects of the granting process, but our evidence suggests that it is not present in the initial round of R01 reviews

A Meta-Analysis of Procedures to Change Implicit Measures

Using a novel technique known as network meta-analysis, we synthesized evidence from 492 studies (87,418 participants) to investigate the effectiveness of procedures in changing implicit measures, which we define as response biases on implicit tasks. We also evaluated these procedures’ effects on explicit and behavioral measures. We found that implicit measures can be changed, but effects are often relatively weak (|ds| \u3c .30). Most studies focused on producing short-term changes with brief, single-session manipulations. Procedures that associate sets of concepts, invoke goals or motivations, or tax mental resources changed implicit measures the most, whereas procedures that induced threat, affirmation, or specific moods/emotions changed implicit measures the least. Bias tests suggested that implicit effects could be inflated relative to their true population values. Procedures changed explicit measures less consistently and to a smaller degree than implicit measures and generally produced trivial changes in behavior. Finally, changes in implicit measures did not mediate changes in explicit measures or behavior. Our findings suggest that changes in implicit measures are possible, but those changes do not necessarily translate into changes in explicit measures or behavior

A gender bias habit-breaking intervention led to increased hiring of female faculty in STEMM departments

Addressing the underrepresentation of women in science is a top priority for many institutions, but the majority of efforts to increase representation of women are neither evidence-based nor rigorously assessed. One exception is the gender bias habit-breaking intervention (Carnes et al., 2015), which, in a cluster-randomized trial involving all but two departmental clusters (N = 92) in the 6 STEMM focused schools/colleges at the University of Wisconsin – Madison, led to increases in gender bias awareness and self-efficacy to promote gender equity in academic science departments. Following this initial success, the present study compares, in a preregistered analysis, hiring rates of new female faculty pre- and post-manipulation. Whereas the proportion of women hired by control departments remained stable over time, the proportion of women hired by intervention departments increased by an estimated 18 percentage points (OR = 2.23, dOR = 0.34). Though the preregistered analysis did not achieve conventional levels of statistical significance (p \u3c 0.07), our study has a hard upper limit on statistical power, as the cluster-randomized trial has a maximum sample size of 92 departmental clusters. These patterns have undeniable practical significance for the advancement of women in science, and provide promising evidence that psychological interventions can facilitate gender equity and diversity

The Psychological Science Accelerator: Advancing Psychology through a Distributed Collaborative Network

Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability