arXiv:2605.20612v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) have emerged as a prominent paradigm for interpretable deep learning, learning by grounding predictions in human-understandable concepts. However, their practical deployment is hindered by the high cost of test-time intervention, as correcting model errors typically requires human experts to manually inspect and verify a large set of predicted concepts. Existing approaches suffer from a fundamental structural limitation: they either adopt a single static concept set, forcing experts to exhaustively annotate concep
The continuous push for more interpretable and reliable AI systems, especially in high-stakes environments, is driving innovations in CBMs.
Improving the interpretability and correctability of AI through concepts is crucial for broader AI adoption, particularly in regulated industries and for complex decision-making.
This research suggests a potential pathway to more practical and scalable interpretable AI, reducing the heavy human intervention previously required for CBMs.
- · AI developers
- · Industries requiring explainable AI (e.g., healthcare, finance)
- · AI governance & ethics organizations
- · AI systems lacking interpretability
Reduced operational costs for deploying interpretable AI in production environments.
Increased trust and adoption of AI systems in critical applications due to improved transparency and error correction.
New regulatory frameworks and compliance standards emerging around AI interpretability and human-in-the-loop validation.
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Read at arXiv cs.LG