3 papers
cs.HC2026
Too Much of the Same: From Algorithmic to Human Bias in Learning to Defer
Dario Pesenti, Alessandro Bogani, Stefano Teso +1
Learning to Defer (LtD) extends supervised learning by allowing a Machine Learning (ML) model to defer harder or less confident decisions to a human expert. Despite being geared fo…
cs.HC2026
Are Concept Bottleneck Models Effective as Decision-Support Systems?
Alessandro Bogani, Nicola Debole, Emanuele Marconato +3
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 allow…
cs.AI2025
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…