Bayes-Optimal Classifiers under Group Fairness

2022-02-20 · Xianli Zeng, Edgar Dobriban, Guang Cheng

Machine learning algorithms are becoming integrated into more and more high-stakes decision-making processes, such as in social welfare issues. Due to the need of mitigating the potentially disparate impacts from algorithmic predictions, many approaches have been proposed in the emerging area of fair machine learning. However, the fundamental problem of characterizing Bayes-optimal classifiers under various group fairness constraints has only been investigated in some special cases. Based on the classical Neyman-Pearson argument (Neyman and Pearson, 1933; Shao, 2003) for optimal hypothesis testing, this paper provides a unified framework for deriving Bayes-optimal classifiers under group fairness. This enables us to propose a group-based thresholding method we call FairBayes, that can directly control disparity, and achieve an essentially optimal fairness-accuracy tradeoff. These advantages are supported by thorough experiments.

📄 PDF Abstract BibTeX arXiv:2202.09724

Code (1)

xianlizeng/fairbayes 공식 구현 pytorch

Tasks

BIG-bench Machine Learning Decision Making Fairness

Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing

2024-02-05 · Xianli Zeng, Guang Cheng, Edgar Dobriban

Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairne…

Attribute Classification Fairness LEMMA

Bayes-Optimal Fair Classification with Multiple Sensitive Features

2025-05-01 · Yi Yang, Yinghui Huang, Xiangyu Chang

Existing theoretical work on Bayes-optimal fair classifiers usually considers a single (binary) sensitive feature. In practice, individuals are often defined by multiple sensitive features. In this paper, we characterize…

Attribute Classification Fairness

FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning

2025-06-04 · Li Zhang, Zhongxuan Han, Chaochao Chen, Xiaohua Feng 외

With emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e.g., female, male). Current research predomi…

Fairness Federated Learning Multi-class Classification

Fair Bayes-Optimal Classifiers Under Predictive Parity

2022-05-15 · Xianli Zeng, Edgar Dobriban, Guang Cheng

Increasing concerns about disparate effects of AI have motivated a great deal of work on fair machine learning. Existing works mainly focus on independence- and separation-based measures (e.g., demographic parity, equali…

Inherent Tradeoffs in Learning Fair Representations

2019-06-19 · NeurIPS 2019 12 · Han Zhao, Geoffrey J. Gordon

Real-world applications of machine learning tools in high-stakes domains are often regulated to be fair, in the sense that the predicted target should satisfy some quantitative notion of parity with respect to a protecte…

Attribute Fairness