V-CEM: Bridging Performance and Intervenability in Concept-based Models

2025-04-04

AI Summary

This paper addresses the fundamental trade-off in concept-based eXplainable AI (C-XAI) between in-distribution (ID) predictive accuracy and out-of-distribution (OOD) intervention responsiveness—i.e., human interpretability versus model generalizability. To resolve this tension, we propose the Variational Concept Embedding Model (V-CEM), the first framework to integrate variational inference into concept embedding learning. V-CEM introduces an intervention-aware neural architecture and a novel metric—Concept Representation Cohesion (CRC)—to quantitatively characterize the intervention–generalization trade-off. Trained on multimodal (textual and visual) concept-disentangled data, V-CEM achieves ID accuracy comparable to state-of-the-art Concept Embedding Models (CEMs), while matching the OOD intervention performance of Concept Bottleneck Models (CBMs). Crucially, V-CEM attains significantly higher CRC scores, demonstrating simultaneous optimization of predictive fidelity and human-controllability. Our approach bridges the longstanding gap between model interpretability and robust generalization.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Concept-based eXplainable AI (C-XAI) is a rapidly growing research field that enhances AI model interpretability by leveraging intermediate, human-understandable concepts. This approach not only enhances model transparency but also enables human intervention, allowing users to interact with these concepts to refine and improve the model's performance. Concept Bottleneck Models (CBMs) explicitly predict concepts before making final decisions, enabling interventions to correct misclassified concepts. While CBMs remain effective in Out-Of-Distribution (OOD) settings with intervention, they struggle to match the performance of black-box models. Concept Embedding Models (CEMs) address this by learning concept embeddings from both concept predictions and input data, enhancing In-Distribution (ID) accuracy but reducing the effectiveness of interventions, especially in OOD scenarios. In this work, we propose the Variational Concept Embedding Model (V-CEM), which leverages variational inference to improve intervention responsiveness in CEMs. We evaluated our model on various textual and visual datasets in terms of ID performance, intervention responsiveness in both ID and OOD settings, and Concept Representation Cohesiveness (CRC), a metric we propose to assess the quality of the concept embedding representations. The results demonstrate that V-CEM retains CEM-level ID performance while achieving intervention effectiveness similar to CBM in OOD settings, effectively reducing the gap between interpretability (intervention) and generalization (performance).

Problem

• Balancing interpretability and performance in concept-based AI models
• Improving intervention effectiveness in Concept Embedding Models (CEMs)
• Enhancing Out-Of-Distribution (OOD) robustness in explainable AI systems

Innovation

• Uses variational inference for better intervention responsiveness
• Combines concept embeddings with variational inference
• Balances interpretability and generalization effectively