Don't let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox
Jad Salem, Deven R. Desai, Swati Gupta

TL;DR
This paper proposes a bias-aware, legally compliant algorithmic approach using banding and partially ordered sets to address employment inequality, balancing legal risks and proactive diversity measures.
Contribution
It introduces a novel legal and algorithmic framework that enables bias mitigation in hiring while respecting legal constraints, extending the use of banding and uncertainty modeling.
Findings
Legal approach using banding is compatible with employment law.
Partially ordered sets effectively handle evaluation uncertainty.
Bias mitigation can be achieved without violating legal standards.
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…
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Taxonomy
TopicsMerger and Competition Analysis · Law, Economics, and Judicial Systems · Legal and Constitutional Studies
