Bias in Implicit Measures as Instances of Biased Behavior under Suboptimal Conditions in the Laboratory

The target paper of Gawronski, Ledgerwood, and Eastwick (this issue) provides a valuable contribution to the literature on implicit bias (IB). We find ourselves in agreement with many of the points that the authors put forward. Most importantly, we agree that it is important to realize that scores on implicit measurement tasks such as the Implicit Association Test (IAT) cannot by default be interpreted as instances of unconscious bias. We also agree that the focus on bias in implicit measures (BIM) may have slowed progress in research on IB, that the focus of bias research should be on reducing real-world instances of bias, and that societal dispar-ities can result in social discrimination in a way that is not captured by the psychological concept of bias. We are happy to see that Gawronski et al. share many aspects of our perspective on IB and implicit measures (see De Houwer, 2006, 2014, 2019; De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009, De Houwer, Gawronski, & Barnes-Holmes, 2013; De Houwer, Van Dessel, & Moran, 2021). Most importantly, (a) IB can indeed be conceived of as a behavioral phenomenon that refers to the impact of social cues on behavior, and (b) implicit measures are not the same as indirect measures, nor do they necessarily reflect associative processes. In sum, we support much of what Gawronski et al. put forward in their target paper. Nevertheless, we also disagree with Gawronski et al. (this issue) on some points. First, we continue to believe IB should not be limited to unconscious bias but should include also instances of bias that are automatic in other ways (e.g., unintentional). Second, we continue to see a potential role for BIM in research on IB, more specifically as an educational tool and as a lab model of IB in the real world.