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.

While schema theory motivated the original measures of automatic cognitive associations between constructs in memory, researchers soon modified these to explore a different domain: implicit attitudes about social groups that elude standard self-reports. As the so-called implicit attitude revolution gained steam, the original measurement goal got much less attention, especially in political science. We believe the schema concept – automatic cognitive associations between features of an attitude object – continues to hold great value for political psychology. We offer a retrofit of the popular implicit association test (IAT), one more efficient than many lexical tasks, to tap these associations in surveys. The new technique captures the degree to which citizens link ideas about ostensibly group-neutral policies to specific social categories. We use this measurement strategy to explore the psychological mechanisms underlying group centrism in politics, an effort that has been largely abandoned due to measurement difficulties. Results from four studies offer practical suggestions about the application of implicit measures for capturing the automatic ways people link groups to important political objects. We conclude by discussing the broader promise of implicit measurement of group schemas, not just implicit affect, for political psychology.