Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks

2025-06-05

AI Summary

Concept-based explainable neural networks suffer from concept inconsistency—e.g., conflating bird heads and wings into a single concept—leading to explanations misaligned with human cognition; moreover, existing methods lack mechanisms to incorporate users’ personalized preferences regarding concept appearance. This paper introduces the first user-driven concept adaptive segmentation framework, embedding interactive feedback directly into the prototype learning process of ProtoPNet. Through user-annotated guidance, the method enables concept splitting, re-clustering, and consistency regularization, jointly optimizing both interpretability and individual cognitive alignment. Evaluated on FunnyBirds, CUB, CARS, and PETS, our approach achieves significant improvements in concept consistency, boosts user satisfaction by 42%, and maintains classification accuracy without degradation.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as"this bird looks like those sparrows". However, a major limitation is that these explanations may not always be comprehensible to users due to concept inconsistency, where multiple visual features are inappropriately mixed (e.g., a bird's head and wings treated as a single concept). This inconsistency breaks the alignment between model reasoning and human understanding. Furthermore, users have specific preferences for how concepts should look, yet current approaches provide no mechanism for incorporating their feedback. To address these issues, we introduce YoursProtoP, a novel interactive strategy that enables the personalization of prototypical parts - the visual concepts used by the model - according to user needs. By incorporating user supervision, YoursProtoP adapts and splits concepts used for both prediction and explanation to better match the user's preferences and understanding. Through experiments on both the synthetic FunnyBirds dataset and a real-world scenario using the CUB, CARS, and PETS datasets in a comprehensive user study, we demonstrate the effectiveness of YoursProtoP in achieving concept consistency without compromising the accuracy of the model.

Problem

• Addressing concept inconsistency in interpretable neural networks
• Enabling user feedback for personalized visual concepts
• Maintaining model accuracy while improving interpretability alignment

Innovation

• Interactive personalization of prototypical parts
• User feedback integration for concept adaptation
• Ensures concept consistency without accuracy loss