PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

TL;DR

This paper introduces a unified framework for measuring causality-based fairness in machine learning, addressing the challenge of identifiability and providing a method to bound fairness measures in various situations.

Contribution

It proposes a unified definition of causality-based fairness and a general optimization approach to measure it under unidentifiable conditions.

Findings

Method effectively bounds causality-based fairness measures.

Experiments validate correctness on synthetic and real datasets.

Framework unifies previous causality-based fairness notions.

Abstract

A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observational data, which is a critical barrier to applying these notions to real-world situations. In this paper, we develop a framework for measuring different causality-based fairness. We propose a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness). Based on that, we propose a general method in the form of a constrained optimization problem for bounding the path-specific counterfactual fairness under all unidentifiable situations. Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method.