arXiv:2606.19882v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts. However, existing CBMs are constrained in their ability to generalize beyond a fixed set of predefined classes and the risk of non-concept information leakage, where predictive signals outside the intended concepts are inadvertently exploited. In this paper, we propose Multimodal Concept Bottleneck Model (MM-CBM) to address these issues and extend CBMs into CLIP. MM-CBM utilizes dual Conce
The proliferation of complex AI models creates an urgent need for enhanced interpretability and generalizability, pushing research into areas like Concept Bottleneck Models.
Improved interpretability and reduced information leakage in AI models are critical for robust, trustworthy, and deployable AI systems, especially in sensitive applications.
AI models can become more transparent and reliable by better aligning their internal representations with human-understandable concepts, fostering greater trust and wider adoption.
- · AI developers
- · Organizations deploying AI
- · AI ethics researchers
- · Black box AI models
- · Developers prioritizing performance over interpretability
Increased interpretability allows for easier debugging and auditing of complex AI systems.
Greater trust in AI leads to its faster integration into critical decision-making processes across various industries.
The development of robust interpretable AI frameworks could democratize AI, making sophisticated models accessible and manageable for a broader range of users without deep technical expertise.
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