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.

本论文使用因果分析作为分析机器学习中公平性参数和其他重要指标的权衡的原则方法，并在广泛使用的公平性改进技术上进行了全面的实证研究，提供了可操作的建议，以及在选择最佳公平性改进方法方面提供了多种可能性。