arXiv 19 Jun 2022 · Econometrics · publishedProduction and Operations Management (2024) · 3 citations (OpenAlex)
arXiv:2206.09300 · PDF · DOI · OpenAlex · Extracted main text
We present a data-driven prescriptive framework for fair decisions, motivated by hiring. An employer evaluates a set of applicants based on their observable attributes. The goal is to hire the best candidates while avoiding bias with regard to a certain protected attribute. Simply ignoring the protected attribute will not eliminate bias due to correlations in the data. We present a hiring policy that depends on the protected attribute functionally, but not statistically, and we prove that, among all possible fair policies, ours is optimal with respect to the firm's objective. We test our approach on both synthetic and real data, and find that it shows great practical potential to improve equity for underrepresented and historically marginalized groups.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Zink, A. and Rose, S (2020) Fair regression for health care spending | 1.000 | 8 | 3 | 100% |
| 2 | Chzhen, E., Denis, C., Hebiri, M., Oneto, L., and Pontil, M (2020) Fair regression with Wasserstein barycenters | 0.737 | 3 | 2 | 100% |
| 3 | Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenst… (2017) A convex framework for fair regression | 0.693 | 6 | 1 | 100% |
| 4 | Bertsimas, D. and Kallus, N (2020) From predictive to prescriptive analytics | 0.644 | 2 | 2 | 100% |
| 5 | Bastani, H., Bayati, M., and Khosravi, K (2021) Mostly exploration-free algorithms for contextual bandits | 0.511 | 2 | 1 | 100% |
| 6 | Joseph, M., Kearns, M., Morgenstern, J. H., and Roth, A (2016) Fairness in learning: Classic and contextual bandits | 0.511 | 2 | 1 | 100% |
| 7 | Wightman, L. F (1998) LSAC National Longitudinal Bar Passage Study | 0.511 | 2 | 1 | 100% |
| 8 | Anderson, D., Bjarnadóttir, M. V., Dezso, C. L., and Ross, D. G (2019) On a firm's optimal response to pressure for gender pay equity | 0.405 | 1 | 1 | 100% |
| 9 | Bastani, H. and Bayati, M (2020) Online decision making with high-dimensional covariates | 0.405 | 1 | 1 | 100% |
| 10 | del Barrio, E., Gordaliza, P., and Loubes, J.-M (2020) Review of mathematical frameworks for fairness in machine learning | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 80 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Individual and group fairness in geographical partitioning | 0.843 | 3 | 3 |