Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks

TL;DR

This paper introduces YoursProtoP, an interactive method that personalizes and improves the interpretability of concept-based neural networks by aligning visual concepts with user preferences, ensuring clearer explanations without losing accuracy.

Contribution

The paper presents a novel interactive approach for customizing prototypical parts in neural networks, enhancing concept consistency and user alignment in explanations.

Findings

YoursProtoP improves concept consistency in explanations.

The method maintains model accuracy while personalizing concepts.

User studies confirm enhanced interpretability and user satisfaction.

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.