There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisions regarding the provision of fair ML services. Nonetheless, it is extremely difficult to analyze the trade-offs when there are multiple fairness parameters and other crucial metrics involved, coupled, and even in conflict with one another. This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines. To ractically and effectively conduct causality analysis, we propose a set of domain-specific optimizations to facilitate accurate causal discovery and a unified, novel interface for trade-off analysis based on well-established causal inference methods. We conduct a comprehensive empirical study using three real-world datasets on a collection of widelyused fairness-improving techniques. Our study obtains actionable suggestions for users and developers of fair ML. We further demonstrate the versatile usage of our approach in selecting the optimal fairness-improving method, paving the way for more ethical and socially responsible AI technologies.

翻译：近年来，提升机器学习公平性的研究日益受到关注。尽管公平性改进方法不断涌现，但公平性改进方法应用于机器学习流程时，各因素之间的权衡关系仍缺乏系统性理解。这种理解对于开发者做出关于提供公平机器学习服务的明智决策至关重要。然而，当涉及多个公平性参数与其他关键指标相互耦合甚至冲突时，分析这些权衡关系极为困难。本文采用因果分析作为原则性方法，研究机器学习流程中公平性参数与其他关键指标之间的权衡。为了实用且高效地开展因果分析，我们提出了一系列领域特定优化，以促进准确的因果发现，并基于成熟的因果推断方法设计了一个统一的新型权衡分析接口。我们使用三个真实世界数据集，对一组广泛使用的公平性改进技术进行了全面实证研究。研究结果为公平性机器学习的用户和开发者提供了可操作的指导。我们进一步展示了该方法在选取最优公平性改进技术中的多样化应用，为更具伦理和社会责任的人工智能技术铺平道路。