One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification

TL;DR

This paper introduces a novel 'One-vs.-One' mitigation method for intersectional bias in fairness-aware binary classification, effectively reducing bias across multiple sensitive attributes in various settings and datasets.

Contribution

The paper proposes a new comparison-based mitigation approach for intersectional bias, extending fairness methods to handle multiple sensitive attributes more effectively.

Findings

Significantly reduces intersectional bias across all tested metrics.

Outperforms conventional fairness methods in diverse experimental settings.

Enhances the applicability of fairness-aware classification to real-world problems with multiple sensitive attributes.

Abstract

With the widespread adoption of machine learning in the real world, the impact of the discriminatory bias has attracted attention. In recent years, various methods to mitigate the bias have been proposed. However, most of them have not considered intersectional bias, which brings unfair situations where people belonging to specific subgroups of a protected group are treated worse when multiple sensitive attributes are taken into consideration. To mitigate this bias, in this paper, we propose a method called One-vs.-One Mitigation by applying a process of comparison between each pair of subgroups related to sensitive attributes to the fairness-aware machine learning for binary classification. We compare our method and the conventional fairness-aware binary classification methods in comprehensive settings using three approaches (pre-processing, in-processing, and post-processing), six metrics (the ratio and difference of demographic parity, equalized odds, and equal opportunity), and two real-world datasets (Adult and COMPAS). As a result, our method mitigates the intersectional bias much better than conventional methods in all the settings. With the result, we open up the potential of fairness-aware binary classification for solving more realistic problems occurring when there are multiple sensitive attributes.