Standardizing Biases: Selection Devices and the Quantification of Race

Author:

Hirschman Daniel1,Bosk Emily Adlin2

Affiliation:

1. Brown University, Providence, RI, USA

2. Rutgers University, New Brunswick, NJ, USA

Abstract

Racial inequality persists despite major advances in formal, legal equality. Scholars and policymakers argue that individual biases (both explicit and implicit) combine with subjective organizational decision-making practices to perpetuate racial inequality. The standardization of decision making offers a potential solution, promising to eliminate the subjectivity that biases consequential decisions. We ask, under what conditions may standardization reduce racial inequality? Drawing on research in science studies and law and society, we argue that standardization must be understood as a heterogeneous practice capable of producing very different outcomes depending on the details of the standard and the organizational infrastructure surrounding its use. We compare selection devices—simple quantified tools for making allocation decisions—in undergraduate admissions and child welfare to highlight the complex relationships between race and standardization. Child welfare agencies adopted a colorblind actuarial device that attempted to predict which children were most at risk and then make decisions based on those predictions. In contrast, the University of Michigan’s points system explicitly considered and valued race, with the goal of increasing minority student enrollments in the context of promoting student body diversity. Comparing these cases demonstrates how actuarial standardization practices, including those adopted with the intention of reducing racial inequality, tend to reinforce an unequal status quo by ideologically reconfiguring mutable social structures into immutable individual risk factors. In contrast, nonactuarial practices that explicitly promote racial equality are vulnerable to political challenges as they violate norms of colorblindness and cannot be defended in terms of their predictive validity.

Funder

National Science Foundation

doris duke charitable foundation

Fahs-Beck Fund for Research and Experimentation

Publisher

SAGE Publications

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