arXiv:2606.00082v1 Announce Type: new Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stake decisions. Concept bottleneck models (CBM) have recently shown promising performance to provide explainable and accurate predictions for classification problems, based on a bottleneck of high-level concepts. Existing CBM methods rely on a linear aggregation of the concept scores to compute predictions. However, a large number of concepts is often used in this linear approach, which undermines explainability and favors information leakage. In ge
The increasing demand for explainable AI in critical applications drives the continuous refinement of model interpretability techniques.
Improving the explainability and robustness of AI models is crucial for their deployment in high-stakes environments, fostering trust and enabling better decision-making.
This research introduces a method to enhance the explainability of concept bottleneck models by reducing information leakage and the number of required concepts.
- · AI explainability researchers
- · Computer vision developers
- · Industries requiring interpretable AI (e.g., healthcare, defense)
- · Traditional black-box AI models
- · Methods prioritizing prediction accuracy over explainability
Increased adoption of explainable AI in sensitive applications due to enhanced interpretability.
Development of regulatory frameworks for AI that mandate higher levels of explainability.
Public confidence in AI systems grows, leading to broader societal integration of advanced AI technologies.
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