Interpretable Prognostics with Concept Bottleneck Models

Concept Bottleneck Models make AI predictions understandable to experts: Rather than explaining decisions through low-level technical features, this approach uses high-level "concepts" (in this case, different ways machinery degrades) that domain experts can actually understand and verify, making the AI system more trustworthy for safety-critical applications like aircraft engine maintenance.

Interpretability doesn't require sacrificing accuracy: The study demonstrates that these interpretable models perform "on par or superior to black-box models" on aircraft engine remaining useful life prediction, challenging the common assumption that explainability must come at a performance cost.

Domain experts can correct the AI system in real-time: A key advantage of this approach is that once concepts are verified, experts can intervene and adjust the model's reasoning at test-time, enabling human oversight rather than treating the AI as a fixed black box—particularly important for safety-critical decisions.

This approach works even with limited labeled data: The models maintain their interpretability and performance "even when the available labeled concepts are limited," which is practically important since obtaining expert-labeled concept data is often expensive and time-consuming in industrial settings.

Moves beyond surface-level explanations: Traditional explainability techniques only show which input variables matter most, but this method goes deeper by having the AI reason through meaningful intermediate concepts that experts can understand, debate, and validate.