Loss Balancing for Fair Supervised Learning
Loss Balancing for Fair Supervised Learning
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DOI：
10.48550/arxiv.2311.03714
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Mohammad Mahdi Khalili;Xueru Zhang;Mahed Abroshan
中科院分区：
文献类型：
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作者：
Mohammad Mahdi Khalili;Xueru Zhang;Mahed Abroshan
监督的学习模型已用于各种领域，例如贷款，大学录取，面部识别，自然语言处理等。但是，它们可能会继承培训数据中的先前存在的偏见，并对受保护的社会群体表现出歧视。已经提出了各种公平的概念来解决不公平的问题。在这项工作中，我们专注于均衡的损失（EL），这是一个公平的概念，要求（大约）在不同群体之间进行预期损失。在学习过程中强加EL会导致非凸优化问题，即使损失函数是凸的，并且无法正确采用现有的公平学习算法来在EL约束下找到公平的预测因子。本文介绍了一种算法，该算法可以利用现成的凸面编程工具（例如CVXPY）有效地找到该非Convex优化的全局最佳。特别是，我们提出了Elminimizer算法，该算法通过将非凸优化的优化降低到凸优化问题的顺序来找到EL下的最佳公平预测因子。从理论上讲，我们证明我们的算法在某些条件下找到了全局最佳解决方案。然后，我们通过几项实证研究来支持我们的理论结果。
Supervised learning models have been used in various domains such as lending, college admission, face recognition, natural language processing, etc. However, they may inherit pre-existing biases from training data and exhibit discrimination against protected social groups. Various fairness notions have been proposed to address unfairness issues. In this work, we focus on Equalized Loss (EL), a fairness notion that requires the expected loss to be (approximately) equalized across different groups. Imposing EL on the learning process leads to a non-convex optimization problem even if the loss function is convex, and the existing fair learning algorithms cannot properly be adopted to find the fair predictor under the EL constraint. This paper introduces an algorithm that can leverage off-the-shelf convex programming tools (e.g., CVXPY) to efficiently find the global optimum of this non-convex optimization. In particular, we propose the ELminimizer algorithm, which finds the optimal fair predictor under EL by reducing the non-convex optimization to a sequence of convex optimization problems. We theoretically prove that our algorithm finds the global optimal solution under certain conditions. Then, we support our theoretical results through several empirical studies.