arXiv:2606.19489v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space. Recent approaches leverage vision-language models to generate concept embeddings, reducing the need for manual concept annotations. However, these models suffer from a critical limitation: as the number of concepts approaches the embedding dimension, information leakage increases, enabling the model to exploit spurious or semantically irrelevant correlations and undermining interpretability. In this work, we propose
This research addresses a known limitation in current concept-based AI models, indicating a maturing understanding of interpretability challenges within advanced AI systems.
Improving interpretability in AI models is crucial for their deployment in high-stakes environments, fostering trust, and enabling better decision-making and debugging.
The proposed 'Concept Flow Models' offer a method to build more robust and reliable interpretable AI systems by mitigating information leakage, enhancing their practical applicability.
- · AI researchers
- · AI ethics and safety organizations
- · Developers of explainable AI (XAI) tools
- · Industries requiring transparent AI
- · AI models without robust interpretability
- · Systems relying on spurious correlations for performance
AI models become more transparent, allowing developers and users to understand their reasoning better.
Increased adoption of interpretable AI leads to greater trust and broader use in sensitive applications.
Improved debugging and auditing capabilities for AI systems could accelerate AI development and reduce deployment risks.
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Read at arXiv cs.LG