arXiv:2606.04364v1 Announce Type: cross Abstract: Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable. On fine-grained recognition tasks the concept heads are usually free to attend anywhere in the image, so a head named for one body region can be satisfied by evidence on another. This work studies a part-factorized CBM that removes that freedom by construction. The method has three components built on a frozen DINOv3 vision transformer. A learned foreground gate, trained on DINOv3 patch features, suppresse
The continuous drive for more explainable and auditable AI systems is leading to innovations in model architectures like Concept Bottleneck Models, addressing growing demands for transparency.
Improved interpretability in AI models is crucial for deployment in sensitive applications, fostering trust, and enabling better debugging and compliance with future regulations.
AI models can now be designed with built-in mechanisms for spatially grounded concept attribution, reducing ambiguity in their decision-making processes.
- · AI developers
- · Regulatory bodies
- · Industries requiring auditable AI
- · Black-box AI models
- · Systems lacking transparency
Increased adoption of Concept Bottleneck Models in computer vision tasks.
Development of standardized protocols for auditing spatially grounded AI decisions.
Enhanced public trust in AI systems leading to broader integration into critical infrastructure.
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