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.AI2026
Exploring SAIG Methods for an Objective Evaluation of XAI
Miquel Miró-Nicolau, Gabriel Moyà-Alcover, Anna Arias-Duart
The evaluation of eXplainable Artificial Intelligence (XAI) methods is a rapidly growing field, characterized by a wide variety of approaches. This diversity highlights the complex…
cs.CV2024
Towards a Novel Measure of User Trust in XAI Systems
Miquel Miró-Nicolau, Gabriel Moyà-Alcover, Antoni Jaume-i-Capó +4
The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (X…