Contrastive Explainable Clustering with Differential Privacy
This work addresses the need for privacy-aware machine learning by offering personalized and privacy-preserving explanations in clustering tasks, representing an incremental advancement in the field.
The paper tackled the problem of providing explainable AI for clustering algorithms while ensuring differential privacy, achieving results where privacy-preserving explanations maintained similar utility bounds as non-private ones across various datasets.
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.