Fair Measures: A Behavioral Realist Revision of "Affirmative Action"
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CitationJerry Kang and Mahzarin R. Banaji, Fair Measures: A Behavioral Realist Revision of Affirmative Action, 94 Cal. L. Rev. 1063 (2006).
AbstractNew facts recently discovered in the mind and behavioral sciences have the potential to transform both lay and expert conceptions of affirmative action. Drawing on recent findings in implicit social cognition (ISC) and applying a legal methodology called behavioral realism, the authors advance four arguments. First, evidence of pervasive implicit bias allows us to avoid problematic backward- and forward-looking justifications for affirmative action and instead focus on addressing discrimination here and now. Second, evidence of biased interpretation and stereotype threat suggests that merit is currently being mismeasured, and that more accurate measurement processes should be adopted. Third, evidence of the malleability of implicit bias suggests interventions different from the traditional social contact hypothesis, such as deploying debiasing agents. Finally, instead of an arbitrary deadline, a better terminus for various affirmative action programs is when our society reaches alignment between explicit normative commitments and measures of implicit bias. Through this analysis of the legal and policy implications of cutting-edge social cognitive research, the authors shed the freighted term affirmative action and produce instead a scientific and normative common ground in favor of fair measures.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12220342
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