106 lines
3.4 KiB
YAML
106 lines
3.4 KiB
YAML
abstract: 'As a vast and ever-growing body of social-scientific research shows,
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discrimination remains pervasive in the United States. In education,
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work, consumer markets, healthcare, criminal justice, and more, Black
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people fare worse than whites, women worse than men, and so on.
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Moreover, the evidence now convincingly demonstrates that this
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inequality is driven by discrimination. Yet solutions are scarce. The
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best empirical studies find that popular interventions-like diversity
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seminars and antibias trainings-have little or no effect. And more
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muscular solutions-like hiring quotas or school busing-are now regularly
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struck down as illegal. Indeed, in the last thirty years, the Supreme
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Court has invalidated every such ambitious affirmative action plan that
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it has reviewed. This Article proposes a novel solution: Big Data
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Affirmative Action. Like old-fashioned affirmative action, Big Data
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Affirmative Action would award benefits to individuals because of their
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membership in protected groups. Since Black defendants are
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discriminatorily incarcerated for longer than whites, Big Data
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Affirmative Action would intervene to reduce their sentences. Since
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women are paid less than men, it would step in to raise their salaries.
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But unlike old-fashioned affirmative action, Big Data Affirmative Action
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would be automated, algorithmic, and precise. Circa 2021, data
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scientists are already analyzing rich datasets to identify and quantify
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discriminatory harm. Armed with such quantitative measures, Big Data
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Affirmative Action algorithms would intervene to automatically adjust
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flawed human decisions-correcting discriminatory harm but going no
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further. Big Data Affirmative Action has two advantages over the
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alternatives. First, it would actually work. Unlike, say, antibias
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trainings, Big Data Affirmative Action would operate directly on unfair
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outcomes, immediately remedying discriminatory harm. Second, Big Data
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Affirmative Action would be legal, notwithstanding the Supreme Court''s
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recent case law. As argued here, the Court has not, in fact, recently
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turned against affirmative action. Rather, it has consistently demanded
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that affirmative action policies both stand on solid empirical ground
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and be well tailored to remedying only particularized instances of
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actual discrimination. The policies that the Court recently rejected
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have failed to do either. Big Data Affirmative Action can easily do
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both.'
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affiliation: 'Salib, PN (Corresponding Author), Univ Houston, Law Ctr, Law, Houston,
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TX 77004 USA.
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Salib, PN (Corresponding Author), Univ Houston, Hobby Sch Publ Affairs, Houston,
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TX 77004 USA.
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Salib, Peter N., Univ Houston, Law Ctr, Law, Houston, TX 77004 USA.
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Salib, Peter N., Univ Houston, Hobby Sch Publ Affairs, Houston, TX 77004 USA.'
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author: Salib, Peter N.
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author_list:
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- family: Salib
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given: Peter N.
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da: '2023-09-28'
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files: []
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issn: 0029-3571
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journal: NORTHWESTERN UNIVERSITY LAW REVIEW
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keywords-plus: 'RACIAL-DISCRIMINATION; DISPARITIES; MARKET; EMPLOYMENT; IMPACT; BLACK;
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BIAS; RACE'
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language: English
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number: '3'
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number-of-cited-references: '124'
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pages: 821-892
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papis_id: 23fe2c10bfce3224e665b7467814158e
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ref: Salib2022bigdata
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times-cited: '0'
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title: BIG DATA AFFIRMATIVE ACTION
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type: article
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unique-id: WOS:000885982100004
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usage-count-last-180-days: '1'
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usage-count-since-2013: '5'
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volume: '117'
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web-of-science-categories: Law
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year: '2022'
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