Contrastive Explainable Clustering with Differential Privacy

TL;DR

This paper introduces a privacy-preserving method for contrastive explanations in clustering that maintains utility while providing personalized insights, advancing privacy-aware explainable AI.

Contribution

It combines differential privacy with contrastive explanations for clustering, demonstrating that privacy does not significantly reduce explanation utility.

Findings

Differential privacy does not significantly impact clustering utility.

The method provides personalized, meaningful explanations.

Experiments confirm effectiveness across various datasets.

Abstract

This paper presents a novel approach to Explainable AI (XAI) that combines contrastive explanations with differential privacy for clustering algorithms. Focusing on k-median and k-means problems, we calculate contrastive explanations as the utility difference between original clustering and clustering with a centroid fixed to a specific data point. This method provides personalized insights into centroid placement. Our key contribution is demonstrating that these differentially private explanations achieve essentially the same utility bounds as non-private explanations. Experiments across various datasets show that our approach offers meaningful, privacy-preserving, and individually relevant explanations without significantly compromising clustering utility. This work advances privacy-aware machine learning by balancing data protection, explanation quality, and personalization in clustering tasks.