Minimax AUC Fairness: Efficient Algorithm with Provable Convergence Article Swipe

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Zhenhuan Yang , Yan Lok Ko , Kush R. Varshney , Yiming Ying · YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2208.10451

The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AUC) is widely used to evaluate the performance of a scoring function in machine learning, but is studied in algorithmic fairness less than other performance metrics. Due to the pairwise nature of the AUC, defining an AUC-based group fairness metric is pairwise-dependent and may involve both \emph{intra-group} and \emph{inter-group} AUCs. Importantly, considering only one category of AUCs is not sufficient to mitigate unfairness in AUC optimization. In this paper, we propose a minimax learning and bias mitigation framework that incorporates both intra-group and inter-group AUCs while maintaining utility. Based on this Rawlsian framework, we design an efficient stochastic optimization algorithm and prove its convergence to the minimum group-level AUC. We conduct numerical experiments on both synthetic and real-world datasets to validate the effectiveness of the minimax framework and the proposed optimization algorithm.

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preprint

Language

en

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http://arxiv.org/abs/2208.10451

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https://arxiv.org/pdf/2208.10451

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green

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1

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10

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https://openalex.org/W4292945935

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OpenAlex ID

https://openalex.org/W4292945935

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DOI

https://doi.org/10.48550/arxiv.2208.10451

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Title

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence

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Type

preprint

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Language

en

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Publication year

2022

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Publication date

2022-08-22

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Authors

Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying

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https://arxiv.org/abs/2208.10451

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https://arxiv.org/pdf/2208.10451

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Yes

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green

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https://arxiv.org/pdf/2208.10451

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Concepts

Minimax, Pairwise comparison, Metric (unit), Convergence (economics), Machine learning, Computer science, Mathematical optimization, Group (periodic table), Algorithm, Artificial intelligence, Mathematics, Economics, Operations management, Organic chemistry, Economic growth, Chemistry

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Cited by

1

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2025: 1

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10

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