A Computational Approach to Improving Fairness in K-means Clustering

2025-05-29

AI Summary

K-means clustering often compromises group fairness due to imbalanced distributions of sensitive attributes (e.g., gender, race). To address this, we propose a two-stage fair optimization framework: first perform standard K-means, then selectively reassign only a small number of critical samples to improve fairness. We introduce a novel “fairness cost” metric and design two efficient, high-impact sample selection strategies—neighborhood boundary point identification and high-mixing-degree point selection—to jointly enhance fairness while preserving clustering quality. Our method integrates subgroup distribution constraints, local neighborhood search, and quantitative mixing-degree analysis, ensuring extensibility in both algorithmic design and fairness metric adoption. Experiments on multiple benchmark datasets demonstrate substantial improvements in fairness metrics (e.g., balance, statistical parity), with less than 1% degradation in the clustering objective function value—validating its effectiveness and practicality.

Technology Category

• Machine Learning: Clustering
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster membership of a small subset of selected data points. Two computationally efficient algorithms are proposed in identifying those data points that are expensive for fairness, with one focusing on nearest data points outside of a cluster and the other on highly 'mixed' data points. Experiments on benchmark datasets show substantial improvement on fairness with a minimal impact to clustering quality. The proposed algorithms can be easily extended to a broad class of clustering algorithms or fairness metrics.

Problem

• Addressing fairness issues in K-means clustering
• Reducing bias from sensitive variables like gender or race
• Optimizing cluster membership with minimal quality impact

Innovation

• Two-stage optimization for fairness improvement
• Efficient algorithms for expensive data points
• Extensible to various clustering algorithms