从稀疏性视角统一评估算法公平性，为多场景应用提供新思路。
Toward Unifying Group Fairness Evaluation from a Sparsity Perspective
- 基于稀疏性构建统一的公平性评估框架
- 在多种数据集和去偏方法上验证有效性
- 适合关注公平性评估与社会公平的研究者
确保算法公平性仍是机器学习中的重大挑战，尤其当模型被广泛应用于不同领域时。尽管存在多种公平性标准，但它们在不同机器学习任务间缺乏通用性。本文探讨了各类稀疏性度量在促进公平性方面的联系与差异，提出一种基于稀疏性的统一公平性评估框架。该框架与现有公平性标准一致，并展现出对多种机器学习任务的广泛适用性。通过在多种数据集和去偏方法上的大量实验，验证了该框架作为评估指标的有效性。本工作通过稀疏性与社会公平的视角重新审视算法公平性，为公平性研究与应用带来潜在广泛影响。
原文摘要 · Abstract (English)
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.
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