提出通用公平分类框架，实现基于分块的模型公平性优化
FairGLVQ: Fairness in Partition-Based Classification
- 构建不依赖特定公平定义的分块模型公平化通用框架
- 推导出公平版学习向量量化（FairGLVQ）算法并验证效果
- 适用于医疗、招聘等重视公平性的实际场景
公平性是社会各领域的重要目标，涵盖教育分配、招聘与薪酬、税收、立法及司法等。随着机器学习在健康、安全和公平等日常领域的广泛应用，公平机器学习研究日益重要。本文聚焦分块与原型基模型的公平性问题。贡献有二：一是提出一种不依赖特定公平定义的分块模型公平机器学习通用框架；二是推导出学习向量量化（LVQ）的公平版本作为具体实例。在理论和真实数据集上与现有算法对比，验证了该方法的实际有效性。
原文摘要 · Abstract (English)
Fairness is an important objective throughout society. From the distribution of limited goods such as education, over hiring and payment, to taxes, legislation, and jurisprudence. Due to the increasing importance of machine learning approaches in all areas of daily life including those related to health, security, and equity, an increasing amount of research focuses on fair machine learning. In this work, we focus on the fairness of partition- and prototype-based models. The contribution of this work is twofold: 1) we develop a general framework for fair machine learning of partition-based models that does not depend on a specific fairness definition, and 2) we derive a fair version of learning vector quantization (LVQ) as a specific instantiation. We compare the resulting algorithm against other algorithms from the literature on theoretical and real-world data showing its practical relevance.
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