1 citations · 1 across the 8 of their papers we have counts for
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Human Cognitive Biases in Explanation-Based Interaction: The Case of Within and Between Session Order Effect
Dario Pesenti, Alessandro Bogani, Katya Tentori +1
Explanatory Interactive Learning (XIL) is a powerful interactive learning framework designed to enable users to customize and correct AI models by interacting with their explanatio…
Knowing What You Cannot Explain: Learning to Reject Low-Quality Explanations
Luca Stradiotti, Dario Pesenti, Stefano Teso +1
Learning to Reject (LtR) frameworks allow ML models to abstain from uncertain predictions and promote user trust. However, since current LtR strategies focus solely on predictive p…
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
Tomasz Michalski, Adam Wróbel, Andrea Bontempelli +6
Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as "thi…
If Concept Bottlenecks are the Question, are Foundation Models the Answer?
Nicola Debole, Pietro Barbiero, Francesco Giannini +3
Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-l…