wow-inequalities/02-data/intermediate/wos_sample/d913240ab756204ec3f91ece1ab53b93-salib-peter-n./info.yaml

abstract: 'As a vast and ever-growing body of social-scientific research shows,

  discrimination remains pervasive in the United States. In education,

  work, consumer markets, healthcare, criminal justice, and more, Black

  people fare worse than whites, women worse than men, and so on.

  Moreover, the evidence now convincingly demonstrates that this

  inequality is driven by discrimination. Yet solutions are scarce. The

  best empirical studies find that popular interventions-like diversity

  seminars and antibias trainings-have little or no effect. And more

  muscular solutions-like hiring quotas or school busing-are now regularly

  struck down as illegal. Indeed, in the last thirty years, the Supreme

  Court has invalidated every such ambitious affirmative action plan that

  it has reviewed. This Article proposes a novel solution: Big Data

  Affirmative Action. Like old-fashioned affirmative action, Big Data

  Affirmative Action would award benefits to individuals because of their

  membership in protected groups. Since Black defendants are

  discriminatorily incarcerated for longer than whites, Big Data

  Affirmative Action would intervene to reduce their sentences. Since

  women are paid less than men, it would step in to raise their salaries.

  But unlike old-fashioned affirmative action, Big Data Affirmative Action

  would be automated, algorithmic, and precise. Circa 2021, data

  scientists are already analyzing rich datasets to identify and quantify

  discriminatory harm. Armed with such quantitative measures, Big Data

  Affirmative Action algorithms would intervene to automatically adjust

  flawed human decisions-correcting discriminatory harm but going no

  further. Big Data Affirmative Action has two advantages over the

  alternatives. First, it would actually work. Unlike, say, antibias

  trainings, Big Data Affirmative Action would operate directly on unfair

  outcomes, immediately remedying discriminatory harm. Second, Big Data

  Affirmative Action would be legal, notwithstanding the Supreme Court''s

  recent case law. As argued here, the Court has not, in fact, recently

  turned against affirmative action. Rather, it has consistently demanded

  that affirmative action policies both stand on solid empirical ground

  and be well tailored to remedying only particularized instances of

  actual discrimination. The policies that the Court recently rejected

  have failed to do either. Big Data Affirmative Action can easily do

  both.'
affiliation: 'Salib, PN (Corresponding Author), Univ Houston, Law Ctr, Law, Houston,
  TX 77004 USA.

  Salib, PN (Corresponding Author), Univ Houston, Hobby Sch Publ Affairs, Houston,
  TX 77004 USA.

  Salib, Peter N., Univ Houston, Law Ctr, Law, Houston, TX 77004 USA.

  Salib, Peter N., Univ Houston, Hobby Sch Publ Affairs, Houston, TX 77004 USA.'
author: Salib, Peter N.
author_list:
- family: Salib
  given: Peter N.
da: '2023-09-28'
files: []
issn: 0029-3571
journal: NORTHWESTERN UNIVERSITY LAW REVIEW
keywords-plus: 'RACIAL-DISCRIMINATION; DISPARITIES; MARKET; EMPLOYMENT; IMPACT; BLACK;

  BIAS; RACE'
language: English
number: '3'
number-of-cited-references: '124'
pages: 821-892
papis_id: 23fe2c10bfce3224e665b7467814158e
ref: Salib2022bigdata
times-cited: '0'
title: BIG DATA AFFIRMATIVE ACTION
type: article
unique-id: WOS:000885982100004
usage-count-last-180-days: '1'
usage-count-since-2013: '5'
volume: '117'
web-of-science-categories: Law
year: '2022'