SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability

Concept Bottleneck Models can now show where they're looking: Traditional interpretable AI systems explain decisions using human-understandable concepts, but SL-CBM adds a crucial improvement—it generates spatial maps showing which parts of an image the model actually uses when identifying each concept, making explanations more trustworthy and verifiable.

Better explanations lead to better interventions: The research demonstrates that when AI systems provide faithful spatial explanations of their reasoning, humans can more effectively debug and correct the model's mistakes, which is particularly important for high-stakes applications like medical imaging or autonomous systems.

Accuracy doesn't have to be sacrificed for interpretability: SL-CBM maintains competitive classification performance while significantly improving explanation quality, suggesting that the traditional trade-off between AI transparency and accuracy can be reduced through better technical design.

Spatial alignment matters for real trustworthiness: The paper identifies that previous concept-based models failed at "locality faithfulness"—meaning they couldn't reliably show where in an image they were focusing—which undermines trust even if the final decision is correct; SL-CBM addresses this fundamental gap.

Interpretable AI is becoming more practical for real-world use: By combining concept-level explanations with spatial visualization through cross-attention mechanisms, this work moves explainable AI closer to being deployable in regulated industries where both accuracy and the ability to audit decision-making are legally required.