What Lies Beneath: A Note on the Explainability of Black-box Machine Learning Models for Road Traffic Forecasting. - Alejandro Barredo Arrieta; Ibai Laña; Javier Del Ser (ITSC, 2019)

Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples. - Alejandro Barredo Arrieta; Javier Del Ser (IJCNN, 2020)

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. - Alejandro Barredo Arrieta; Natalia Díaz Rodríguez; Javier Del Ser; Adrien Bennetot; Siham Tabik; Alberto Barbado; Salvador García 001; Sergio Gil-Lopez; Daniel Molina; Richard Benjamins; Raja Chatila 001; Francisco Herrera (Inf. Fusion, 2020)

Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization. - Javier Del Ser; Alejandro Barredo Arrieta; Natalia Díaz Rodríguez; Francisco Herrera; Andreas Holzinger (CoRR, 2022)

On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification. - Alejandro Barredo Arrieta; Sergio Gil-Lopez; Ibai Laña; Miren Nekane Bilbao; Javier Del Ser (Neural Comput. Appl., 2022)

On generating trustworthy counterfactual explanations. - Javier Del Ser; Alejandro Barredo Arrieta; Natalia Díaz Rodríguez; Francisco Herrera; Anna Saranti; Andreas Holzinger (Inf. Sci., 2024)