arXiv:2602.02886v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor to a single expression whose functional form is set a priori, limiting both predictive accuracy and adaptability to diverse user needs. We propose Mixture of Concept Bottleneck Experts (M-CBE), a framework that generalizes existing CBMs along two dimensions: the number of expressions, referred to as experts, employed by the task predictor to map concepts to the tas
The continuous evolution of AI research pushes for more interpretable and adaptable models, with current Concept Bottleneck Models (CBMs) showing limitations in both accuracy and flexibility.
Improving AI interpretability while enhancing predictive accuracy is critical for deploying AI in sensitive domains, increasing trust, and enabling broader adoption across industries.
AI models can now be designed with a mixture of concept-based 'experts,' allowing for greater adaptability and potentially more accurate and human-understandable reasoning paths compared to single-expression CBMs.
- · AI developers
- · Healthcare sector
- · Financial sector
- · Regulatory bodies
- · Black-box AI models
- · AI systems lacking interpretability
More robust and transparent AI systems become feasible, leading to increased trust and faster development cycles in critical applications.
The demand for expertise in designing and evaluating concept-based AI models will grow, potentially fostering new specializations within AI engineering.
Enhanced interpretability could accelerate regulatory approval processes for AI in high-stakes environments, potentially democratizing access to advanced AI for smaller firms.
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Read at arXiv cs.LG