Contrastive Explainable Clustering with Differential Privacy

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.

Authors

• DungXXX Nguyen Department of Computer Science, and Biocomplexity Institute, University of Virginia
• Ariel Vetzler Department of Computer Science, Bar-Ilan University
• Sarit Kraus Bar Ilan University
• Anil Vullikanti University of Virginia

Keywords

• Explainable AI
• Differential Privacy
• Clustering