Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox. Salem, J., Desai, D., & Gupta, S. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, of FAccT '22, pages 651–666, New York, NY, USA, 2022. Association for Computing Machinery. ![Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{10.1145/3531146.3533129,
author = {Salem, Jad and Desai, Deven and Gupta, Swati},
title = {Don’t let Ricci v. DeStefano Hold You Back: A Bias-Aware Legal Solution to the Hiring Paradox},
year = {2022},
isbn = {9781450393522},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3531146.3533129},
doi = {10.1145/3531146.3533129},
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.},
booktitle = {Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency},
pages = {651–666},
numpages = {16},
keywords = {anti-discrimination laws, bias, hiring, resume screening, uncertainty},
location = {<conf-loc>, <city>Seoul</city>, <country>Republic of Korea</country>, </conf-loc>},
series = {FAccT '22}
}
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