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Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox

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Swati GuptaAuthors Info & Claims

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Pages 651 - 666

https://doi.org/10.1145/3531146.3533129

Published: 20 June 2022 Publication History

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Abstract

Companies that try to address inequality in employment face a hiring paradox. Failing to address workforce imbalance can result in legal sanctions and scrutiny, but proactive measures to address these issues might result in the same legal conflict. Recent run-ins of Microsoft and Wells Fargo with the Labor Department’s Office of Federal Contract Compliance Programs (OFCCP) are not isolated and are likely to persist. To add to the confusion, existing scholarship on Ricci v. DeStefano often deems solutions to this paradox impossible. Circumventive practices such as the 4/5ths rule further illustrate tensions between too little action and too much action.

In this work, we give a powerful way to solve this hiring paradox that tracks both legal and algorithmic challenges. We unpack the nuances of Ricci v. DeStefano and extend the legal literature arguing that certain algorithmic approaches to employment are allowed by introducing the legal practice of banding to evaluate candidates. We thus show that a bias-aware technique can be used to diagnose and mitigate “built-in” headwinds in the employment pipeline. We use the machinery of partially ordered sets to handle the presence of uncertainty in evaluations data. This approach allows us to move away from treating “people as numbers” to treating people as individuals—a property that is sought after by Title VII in the context of employment.

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Minow M(2023)Equality, Equity, and Algorithms: Learning from Justice Rosalie AbellaUniversity of Toronto Law Journal10.3138/utlj-2023-006473:Supplement 2(163-178)Online publication date: 1-Sep-2023
https://doi.org/10.3138/utlj-2023-0064
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Index Terms

Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox
1. Applied computing
  1. Enterprise computing
  2. Law, social and behavioral sciences
    1. Law
2. Social and professional topics

Index terms have been assigned to the content through auto-classification.

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FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

June 2022

2351 pages

ISBN:9781450393522

DOI:10.1145/3531146

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Published: 20 June 2022

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FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency

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Cited By

Cachel KRundensteiner ESerra ESpezzano F(2024)Wise Fusion: Group Fairness Enhanced Rank FusionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679649(163-174)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679649
Minow M(2023)Equality, Equity, and Algorithms: Learning from Justice Rosalie AbellaUniversity of Toronto Law Journal10.3138/utlj-2023-006473:Supplement 2(163-178)Online publication date: 1-Sep-2023
https://doi.org/10.3138/utlj-2023-0064
Cachel KRundensteiner EFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Fair&Share: Fast and Fair Multi-Criteria SelectionsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614874(152-162)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614874
Brânzei SKumar NRanade G(2023)Phase Transitions of Diversity in Stochastic Block Model Dynamics2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton)10.1109/Allerton58177.2023.10313364(1-8)Online publication date: 26-Sep-2023
https://doi.org/10.1109/Allerton58177.2023.10313364

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