Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review

TL;DR

This paper provides a comprehensive review of methods that balance individual and group fairness in machine learning, highlighting their theoretical foundations, trade-offs, and open challenges for developing hybrid fairness algorithms.

Contribution

It systematically analyzes hybrid fairness approaches that integrate group and individual fairness, offering insights into their mechanisms, limitations, and future research directions.

Findings

Analyzed various hybrid fairness methods and their theoretical bases.

Identified key trade-offs between individual and group fairness.

Discussed open challenges and future research directions.

Abstract

Algorithmic fairness has become a central concern in computational decision-making systems, where ensuring equitable outcomes is essential for both ethical and legal reasons. Two dominant notions of fairness have emerged in the literature: Group Fairness (GF), which focuses on mitigating disparities across demographic subpopulations, and Individual Fairness (IF), which emphasizes consistent treatment of similar individuals. These notions have traditionally been studied in isolation. In contrast, this survey examines methods that jointly address GF and IF, integrating both perspectives within unified frameworks and explicitly characterizing the trade-offs between them. We provide a systematic and critical review of hybrid fairness approaches, organizing existing methods according to the fairness mechanisms they employ and the algorithmic and mathematical strategies used to reconcile multiple fairness criteria. For each class of methods, we examine their theoretical foundations, optimization mechanisms, and empirical evaluation practices, and discuss their limitations. Additionally, we discuss the challenges and identify open research directions for developing principled, context-aware hybrid fairness methods. By synthesizing insights across the literature, this survey aims to serve as a comprehensive resource for researchers and practitioners seeking to design hybrid algorithms that provide reliable fairness guarantees at both the individual and group levels.