Counterfactual Explanations for Clustering Models

TL;DR

This paper introduces a model-agnostic counterfactual explanation method for clustering models, using a novel soft-scoring approach to improve interpretability and trust in unsupervised learning.

Contribution

It presents a new counterfactual explanation technique for clustering that leverages soft scores and adapts Bayesian methods from supervised learning.

Findings

Soft scores significantly improve explanation quality.

Method performs well on multiple datasets and algorithms.

Enhances trust and interpretability in clustering models.

Abstract

Clustering algorithms rely on complex optimisation processes that may be difficult to comprehend, especially for individuals who lack technical expertise. While many explainable artificial intelligence techniques exist for supervised machine learning, unsupervised learning -- and clustering in particular -- has been largely neglected. To complicate matters further, the notion of a ``true'' cluster is inherently challenging to define. These facets of unsupervised learning and its explainability make it difficult to foster trust in such methods and curtail their adoption. To address these challenges, we propose a new, model-agnostic technique for explaining clustering algorithms with counterfactual statements. Our approach relies on a novel soft-scoring method that captures the spatial information utilised by clustering models. It builds upon a state-of-the-art Bayesian counterfactual generator for supervised learning to deliver high-quality explanations. We evaluate its performance on five datasets and two clustering algorithms, and demonstrate that introducing soft scores to guide counterfactual search significantly improves the results.