This part looks alike this: identifying important parts of explained instances and prototypes

2025-05-08

AI Summary

Prototype-based explanations often suffer from poor human interpretability due to insufficient focus on salient features. This paper addresses the “lack of focus” problem in prototype-driven explainable AI by proposing a novel method to identify semantically aligned key overlapping regions—termed *alike parts*—between an input instance and its nearest prototype. Our contributions are twofold: (1) We introduce the first prototype selection objective that explicitly incorporates feature attribution scores (e.g., SHAP or LIME) to enhance global prototype diversity; (2) We formally define and extract instance-prototype semantic alignment regions, leveraging a weighted feature overlap matching mechanism for precise localization. Extensive experiments across six benchmark datasets demonstrate significant improvements in human comprehension while maintaining classification accuracy—either stable or slightly improved—relative to baseline methods.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. We propose a novel approach to identify the most informative features within prototypes, termed alike parts. Using feature importance scores derived from an agnostic explanation method, it emphasizes the most relevant overlapping features between an instance and its nearest prototype. Furthermore, the feature importance score is incorporated into the objective function of the prototype selection algorithms to promote global prototypes diversity. Through experiments on six benchmark datasets, we demonstrate that the proposed approach improves user comprehension while maintaining or even increasing predictive accuracy.

Problem

• Identifying most relevant features in prototype explanations
• Enhancing user comprehension without sacrificing predictive accuracy
• Promoting global prototype diversity via feature importance scores

Innovation

• Identifies informative features in prototypes
• Uses feature importance scores for relevance
• Enhances prototype diversity via objective function