arXiv:2606.16535v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) are a relevant tool for explainable Artificial Intelligence because they make their predictions through human-interpretable symbols. However, high task accuracy does not guarantee that these symbols are detected faithfully: jointly trained CBMs may encode task-specific shortcuts in the bottleneck, making their explanations unreliable. In this paper, we study concept-detection reliability by swapping independently trained concept detectors and classification heads that share the same symbolic vocabulary. We use the
The increasing adoption and deployment of AI in critical applications necessitate deeper understanding and assurance of its reliability and explainability.
Reliable concept detection in CBMs is crucial for building trust in AI systems, especially those making decisions with significant consequences, and for regulatory compliance.
This research provides a methodology to assess and improve the fidelity of explanations generated by Concept Bottleneck Models, potentially leading to more robust and transparent AI.
- · AI developers
- · AI ethicists
- · Regulatory bodies
- · Industries relying on explainable AI
- · Developers of opaque AI systems
- · Organizations deploying unreliable AI systems
Increased scrutiny and demand for reliable explainable AI in deployment.
Development of industry standards and benchmarks for AI explainability.
Accelerated adoption of transparent AI models over 'black box' approaches in sensitive applications.
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