Causality-Aided Trade-off Analysis for Machine Learning Fairness

TL;DR

This paper introduces a causality-based framework to analyze and understand the complex trade-offs between fairness and other metrics in machine learning pipelines, aiding fair AI development.

Contribution

It proposes a novel causality analysis approach with domain-specific optimizations and a unified interface for trade-off analysis in fair ML, supported by empirical validation.

Findings

Provides actionable insights for fair ML development

Demonstrates effective selection of fairness methods

Enhances understanding of fairness trade-offs

Abstract

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.