Are Concept Bottleneck Models Effective as Decision-Support Systems?
Concept Bottleneck Models (CBMs) are interpretable-by-design neural networks that detect human-understandable concepts from the input and use them to generate predictions. By allowing users to inspect the concepts underlying a prediction and explore how predictions change under alternative concept configurations, CBMs have emerged as one of the most prominent approaches to supporting human-AI collaboration. However, user studies investigating their actual effectiveness as decision-support systems remain limited. We present two large-scale user studies (N participants = 705, N observations = 6,959) evaluating how concept-based explanations and user interventions on the model's concepts aff...
| Authors | Alessandro Bogani, Nicola Debole, Emanuele Marconato et al. |
|---|---|
| Primary category | cs.HC |
| Published | 26 August 2026 |
| Last updated at source | 22 September 2026 |
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