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13 - IAT Scores, Racial Gaps, and Scientific Gaps

from Section III - Challenges of Research on Implicit Bias

Published online by Cambridge University Press:  aN Invalid Date NaN

Jon A. Krosnick
Affiliation:
Stanford University, California
Tobias H. Stark
Affiliation:
Utrecht University, The Netherlands
Amanda L. Scott
Affiliation:
The Strategy Team, Columbus, Ohio
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Summary

In this chapter we identify scientific gaps research to date regarding the ability of IAT scores to explain real world racial gaps. We use the term “IAT scores” rather than “implicit bias” because, as we show: (1) Implicit bias has no consensual scientific definition; (2) A definition offered by Greenwald (2017) is shown to be logically incoherent and empirically unjustified; (3) Exactly what the IAT measures remains unclear. Nonetheless, meta-analyses have shown that IAT scores predict discrimination to a modest extent. Alternative explanations for gaps are briefly reviewed, highlighting that IAT scores offer only one of many possible such explanations. We then present a series of heuristic models that assume that IAT scores can only explain what is left over, after accounting for other explanations of gaps. This review concludes that IAT scores probably explain a modest portion of those gaps. Even if the IAT captures implicit biases, and those implicit biases were completely eliminated, the extent to which racial gaps would be reduced is minimal. We conclude by arguing that, despite its limitations, the IAT should not be abandoned, but that, even after twenty years, much more research is needed to fully understand what the IAT measures and explains.

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Publisher: Cambridge University Press
Print publication year: 2025

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