Last updated November 29, 2022.


Conscious experience tells a compelling, vivid, and incomplete story of how people act. A police officer may explicitly value racial equality, for example, but treat a Black citizen more harshly than a White citizen without a justifiable reason for doing so. In our lab’s research program, we study how hidden or implicit attitudes and beliefs produce these disconnects between values and action. Our work focuses on how change in hidden or implicit phenomena can inform us about the architecture of the mind and point us to solutions for reducing the gap between values and behavior.

Documenting Subtle Intergroup Biases

To combat intergroup biases, one must know how they operate. In one line of research, we document subtle instances of prejudice, stereotyping, and intergroup behavior across a variety of domains. This research has included studying stereotypes in large text corpora of over 1 billion words (Lee et al., in prep), gender stereotypes among surgeons (Salles et al., 2019), a large-scale study of implicit and explicit biases across 12 intergroup topics on over 7 million participants (Ratliff et al., under review), ideologically-motivated biases in sharing political information (Ekstrom & Lai, 2021), and regional analyses of the relationship between racial prejudice and racial disparities in police traffic stops (Ekstrom, Le Forestier, & Lai, 2022).

Documenting bias requires careful attention to measurement. In research on implicit social cognition, we have conducted meta-analyses on the internal consistency and test-retest reliability of implicit measures broadly (Greenwald & Lai, 2020) and within research on intergroup biases specifically (Lai & Wilson, 2021). We have also examined the contribution of non-associative processes in implicit measurement (Calanchini, Sherman, Klauer, & Lai, 2014, Röhner & Lai, 2021). Ongoing measurement-oriented research has also focused on how to improve the psychometric properties of implicit measures. For example, we have tested how to increase the test-retest reliability of implicit measures through latent variable modeling-based approaches that track an individual’s performance across repeated measurements (Carpenter, Goedderz, & Lai, in press).

Effective Change in Implicit Biases

To understand the forces that are most influential in changing implicit associations, we must also learn which specific strategies are most effective. To accomplish this, we organized a research contest to test many interventions simultaneously (Lai et al., 2014). Nine of the eighteen interventions we tested were effective at reducing implicit racial prejudice immediately.

However, temporary malleability of implicit associations does not guarantee long-term change. We sought to see if effective interventions from the research contest were effective in the long-term (Lai et al., 2016).  We tested the nine successful interventions  from the research contest again, and found that none continued to have an effect after a day.  In ongoing research, we are examining whether new approaches (e.g., more intensive interventions, targeting different mechanisms) will reduce implicit bias in the long-term.

We have also found that changes in implicit bias do not necessarily translate to behavioral change In a meta-analysis with 494 experiments (Forscher*, Lai*, et al., 2019). This finding indicates that efforts to reduce implicit biases directly may not necessarily reduce discrimination. Strategies that motivate people to self-regulate their implicit biases or directly target the discriminatory-behavior-of-interest may be more effective (Lai & Banaji, 2020).

Reducing Discrimination in Real-World Settings

Reducing implicit bias will not effectively reduce discrimination in isolation. Effective approaches will require a focus on changing the behaviors that are most closely linked to discrimination on-the-ground.

One line of work has focused on education about implicit bias and diversity training, with a focus on law enforcement. In partnership with the Anti-Defamation League and the Center for Naval Analyses, we are training police officers on the science of hidden biases and educating them on strategies to mitigate their influence (Lai & Lisnek, under review; Lewis, Peyton, & Lai, in prep). Ongoing work is assessing the long-term impact of these trainings on officers’ beliefs and behavior.

Another line of work has examined changes in the ‘choice architecture’ of how people make decisions about others. One recent paper has examined how interventions to reduce discrimination by targeting randomness or noise in decision-making can be as effective as targeting hidden preferences (Axt & Lai, 2019).

* Co-lead-authors.