提出GEG算法，在多分类中同时提升准确率与公平性。
A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks
- 将多分类公平学习建模为有效性与多个线性公平约束的多目标问题
- 在7个多分类和3个二分类数据集上，优于6个基线方法
- 支持多种公平性定义，适用于需兼顾准确与公平的场景
AI与机器学习模型在敏感领域的广泛应用引发对公平性的重大关切。尽管学术界已提出多种缓解二分类任务中偏差的方法，但多分类场景下的公平性问题仍研究不足。本文首次将多分类公平学习建模为有效性（预测准确性）与多个线性公平约束之间的多目标问题。为此，提出广义指数梯度（Generalised Exponentiated Gradient, GEG）算法，该算法为一种内部处理（in-processing）方法，可在多种公平性定义下增强二分类与多分类任务的公平性。我们在七个多分类数据集和三个二分类数据集上，对GEG与六个基线方法进行了全面评估，采用四种广泛使用的有效性指标和三种公平性定义进行比较。
原文摘要 · Abstract (English)
The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness. While the research community has introduced various methods for bias mitigation in binary classification tasks, the issue remains under-explored in multi-class classification settings. To address this limitation, in this paper, we first formulate the problem of fair learning in multi-class classification as a multi-objective problem between effectiveness (i.e., prediction correctness) and multiple linear fairness constraints. Next, we propose a Generalised Exponentiated Gradient (GEG) algorithm to solve this task. GEG is an in-processing algorithm that enhances fairness in binary and multi-class classification settings under multiple fairness definitions. We conduct an extensive empirical evaluation of GEG against six baselines across seven multi-class and three binary datasets, using four widely adopted effectiveness metrics and three fairness definitions.
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