4 papers
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…
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…
Explaining Human Comparisons using Alignment-Importance Heatmaps
Nhut Truong, Dario Pesenti, Uri Hasson
We present a computational explainability approach for human comparison tasks, using Alignment Importance Score (AIS) heatmaps derived from deep-vision models. The AIS reflects a f…