arXiv:2604.16076v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts, but they provide no way to verify whether learned concepts align with the human's intended meaning, hurting interpretability. We introduce Prototype-Grounded Concept Models (PGCMs), which ground concepts in learned visual prototypes: image parts that serve as explicit evidence for the concepts. This grounding enables direct inspection of concept semantics and supports targeted human intervention a
The increasing complexity and opacity of AI models necessitate new methods for interpretability and verification, especially as AI applications become more critical.
This development addresses a fundamental challenge in AI adoption and trust by allowing for direct inspection and validation of how models form decisions, which is crucial for safety and regulatory compliance.
AI models can now be designed with built-in mechanisms for verifiable concept alignment, shifting from opaque 'black box' systems to more transparent and explainable architectures.
- · AI interpretability researchers
- · Developers of safety-critical AI systems
- · Regulatory bodies
- · Auditors of AI models
- · Companies relying solely on 'black box' AI models
- · Those resistant to AI transparency
Increased trust and adoption of advanced AI systems in sensitive domains due to enhanced verifiability.
Development of new AI audit and compliance industries focused on concept alignment and interpretability.
Legislation and standards mandating specific levels of concept verifiability for AI deployed in public services or critical infrastructure.
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Read at arXiv cs.LG