Author

A. Bogani

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Preprint Aug 2026

Are Concept Bottleneck Models Effective as Decision-Support Systems?

Results show that CBMs, and particularly their interactive component, can improve human-AI team accuracy relative to both unaided human performance and performance with non-interpretable AI support, however, these benefits emerge only under certain conditions: classification tasks perceived as difficult, easily identif...

A. Bogani, Nicola Debole, E. Marconato et al. · 2 citations

#human-computer interacti... Preprint Aug 2026

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...

A. Bogani, Nicola Debole, E. Marconato et al. · 0 citations

#human-computer interacti... Preprint Aug 2026

Too Much of the Same: From Algorithmic to Human Bias in Learning to Defer

It is demonstrated that standard LtD strategies show class-dependent sampling bias in classification tasks in practice, and thus may disproportionately defer the minority classes when applied to imbalanced datasets, and that such asymmetries in task delegation may trigger human biases, ultimately leading to poorer down...

Dario Pesenti, A. Bogani, Stefano Teso et al. · 1 citation