Ensuring algorithmic fairness remains a significant challenge in machine learning, particularly as models are increasingly applied across diverse domains. While numerous fairness criteria exist, they often lack generalizability across different machine learning problems. This paper examines the connections and differences among various sparsity measures in promoting fairness and proposes a unified sparsity-based framework for evaluating algorithmic fairness. The framework aligns with existing fairness criteria and demonstrates broad applicability to a wide range of machine learning tasks. We demonstrate the effectiveness of the proposed framework as an evaluation metric through extensive experiments on a variety of datasets and bias mitigation methods. This work provides a novel perspective to algorithmic fairness by framing it through the lens of sparsity and social equity, offering potential for broader impact on fairness research and applications.

翻译：确保算法公平性仍然是机器学习领域的一项重大挑战，尤其是在模型日益广泛应用于不同领域的背景下。尽管存在多种公平性标准，但它们往往缺乏跨不同机器学习问题的普适性。本文探讨了多种稀疏性度量在促进公平性方面的联系与差异，并提出了一种基于稀疏性的统一框架用于评估算法公平性。该框架与现有公平性标准保持一致，并展现出对广泛机器学习任务的普遍适用性。我们通过在多种数据集和偏差缓解方法上进行大量实验，验证了所提框架作为评估指标的有效性。本研究通过从稀疏性和社会公平性的视角重构算法公平性问题，为该领域提供了新的研究视角，有望对公平性研究与应用产生更广泛的影响。