9 citations · 14 across the 4 of their papers we have counts for
5 papers
A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language
Marilyn Bello, Rafael Bello, Maria-Matilde García +3
The growing application of artificial intelligence in sensitive domains has intensified the demand for systems that are not only accurate but also explainable and trustworthy. Alth…
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability
Iván Sevillano-García, Julián Luengo, Francisco Herrera
Out-of-Distribution (OOD) detection is a critical task in machine learning, particularly in safety-sensitive applications where model failures can have serious consequences. Howeve…
X-SHIELD: Regularization for eXplainable Artificial Intelligence
Iván Sevillano-García, Julián Luengo, Francisco Herrera
As artificial intelligence systems become integral across domains, the demand for explainability grows, the called eXplainable artificial intelligence (XAI). Existing efforts prima…
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of study
Iván Sevillano-García, Julián Luengo-Martín, Francisco Herrera
Explainable artificial intelligence is proposed to provide explanations for reasoning performed by an Artificial Intelligence. There is no consensus on how to evaluate the quality…
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
S. Tabik, A. Gómez-Ríos, J. L. Martín-Rodríguez +10
Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT…