arXiv 14 Feb 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2402.08879 · PDF · DOI · OpenAlex · Extracted main text
Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator for the fairness-accuracy frontier introduced by Liang, Lu, Mu, and Okumura (2024). We derive its asymptotic distribution and propose inference methods to test key hypotheses in the fairness literature, such as (i) whether excluding group identity from use in training the algorithm is optimal and (ii) whether there are less discriminatory alternatives to a given algorithm. In addition, we construct an estimator for the distance between a given algorithm and the fairest point on the frontier, and characterize its asymptotic distribution. Using Monte Carlo simulations, we evaluate the finite-sample performance of our inference methods. We apply our framework to re-evaluate algorithms used in hospital care management and show that our approach yields alternative algorithms that lie on the fairness-accuracy frontier, offering improvements along both dimensions.
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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 | Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullain… (2019) Dissecting racial bias in an algorithm used to manage the health of populations | 1.000 | 25 | 4 | 100% |
| 2 | Liang, Annie, Jay Lu, Xiaosheng Mu, and Kyohei Okumura (2024) Algorithm Design: A Fairness-Accuracy Frontier | 1.000 | 16 | 8 | 100% |
| 3 | Fang, Zheng and Andres Santos (2019) Inference on directionally differentiable functions | 1.000 | 16 | 5 | 100% |
| 4 | Kaido, Hiroaki (2016) A dual approach to inference for partially identified econometric models | 1.000 | 11 | 3 | 100% |
| 5 | Molchanov, Ilya and Francesca Molinari (2018) Random Sets in Econometrics self | 1.000 | 8 | 4 | 100% |
| 6 | Semenova, Vira (2023) Debiased machine learning of set-identified linear models | 1.000 | 7 | 5 | 100% |
| 7 | Beresteanu, Arie and Francesca Molinari (2008) Asymptotic Properties for a Class of Partially Identified Models self | 1.000 | 5 | 3 | 100% |
| 8 | Chandrasekhar, Arun, Victor Chernozhukov, Francesca Molinari, and Pa… (2018) Best linear approximations to set identified functions: with an application to the gender wage gap self | 0.928 | 4 | 3 | 100% |
| 9 | Cárcamo, Javier, Antonio Cuevas, and Luis-Alberto Rodríguez (2020) Directional differentiability for supremum-type functionals: Statistical applications | 0.874 | 7 | 2 | 100% |
| 10 | Schneider, Rolf (1993) Convex Bodies: The Brunn-Minkowski Theory | 0.874 | 5 | 2 | 100% |
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