Our understanding of implicit bias and how to measure it has yet to be settled. Various debates between cognitive scientists are unresolved. Moreover, the public’s understanding of implicit bias tests continues to lag behind cognitive scientists’. These discrepancies pose potential problems. After all, a great deal of implicit bias research has been publicly funded. Further, implicit bias tests continue to feature in discourse about public- and private-sector policies surrounding discrimination, inequality, and even the purpose of science. We aim to do (...) our part by reconstructing some of the recent arguments in ordinary language and then revealing some of the operative norms or values that are often hidden beneath the surface of these arguments. This may help the public learn more about the science of implicit bias. It may also help both laypeople and scientists reflect on the values, interests, and stakeholders involved in establishing, justifying, and communicating scientific research. (shrink)

Based on a review of several “anomalies” in research using implicit measures, Machery (2021) dismisses the modal interpretation of participant responses on implicit measures and, by extension, the value of implicit measures. We argue that the reviewed findings are anomalies only for specific—influential but long-contested—accounts that treat responses on implicit measures as uncontaminated indicators of trait-like unconscious representations that coexist with functionally independent conscious representations. However, the reviewed findings are to-be-expected “normalities” when viewed from the perspective of long-standing alternative frameworks (...) that treat responses on implicit measures as the product of dynamic processes that operate on momentarily activated, consciously accessible information. Thus, although we agree with Machery that the modal view is empirically unsupported, we argue that implicit measures can make a valuable contribution to understanding the complexities of human behavior if they are used wisely in a way that acknowledges what they can and cannot do. (shrink)

Michael Brownstein, Alex Madva, and Bertram Gawronski articulate a careful defense of research on implicit bias. They argue that though there is room for improvement in various areas, when we set the bar appropriately and when we are comparing relevant events, the test–retest stability and predictive ability of implicit bias measures are respectable. Edouard Machery disagrees. He argues that theories of implicit bias have failed to answer four fundamental questions about measures of implicit bias, and this undermines their utility in (...) further scientific research and policy making. In this article, I offer my perspective on this important debate. I argue that there is a theoretical mismatch in debating the merits of a research program on the terms of a specific theory within the research program. Nevertheless, the discussion allows us to see which questions are answered from within the perspective of a particular theory. I argue that the emphasis should be on whether implicit bias theories predict novel facts. (shrink)