Toward a Meaningful Metric of Implicit Prejudice

• 成果类型: Article
• 署名作者: Blanton, Hart; Jaccard, James; Strauts, Erin; Mitchell, Gregory; Tetlock, Philip E.
• 署名单位: University of Connecticut; New York University; University of Connecticut; University of Virginia; University of Pennsylvania; University of Pennsylvania
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/a0038379
• 发表日期: 2015
• 页码: 1468-1481
• 关键词: arbitrary metrics; Implicit Association Test; IMPLICIT ATTITUDES; PREJUDICE; DISCRIMINATION

摘要

The modal distribution of the Implicit Association Test (IAT) is commonly interpreted as showing high levels of implicit prejudice among Americans. These interpretations have fueled calls for changes in organizational and legal practices, but such applications are problematic because the IAT is scored on an arbitrary psychological metric. The present research was designed to make the IAT metric less arbitrary by determining the scores on IAT measures that are associated with observable racial or ethnic bias. By reexamining data from published studies, we found evidence that the IAT metric is right biased, such that individuals who are behaviorally neutral tend to have positive IAT scores. Current scoring conventions fail to take into account these dynamics and can lead to faulty inferences about the prevalence of implicit prejudice.