3 papers
cs.AI2026
A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
Miquel Miró-Nicolau, Francesco Spinnato, Riccardo Guidotti
Evaluating explainable Artificial Intelligence (XAI) methods is a challenging task due to the lack of reliable evaluation procedures and, in particular, the absence of ground truth…
cs.LG2025
An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items
Luca Corbucci, Javier Alejandro Borges Legrottaglie, Francesco Spinnato +2
Accurately identifying items forgotten during a supermarket visit and providing clear, interpretable explanations for recommending them remains an underexplored problem within the…
cs.LG2025
MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series
Dawid Płudowski, Francesco Spinnato, Piotr Wilczyński +4
Counterfactual explanations provide an intuitive way to understand model decisions by identifying minimal changes required to alter an outcome. However, applying counterfactual met…