A Methodology based on Rebalancing Techniques to Measure and Improve Fairness in Artificial Intelligence algorithms. - Ana Lavalle; Alejandro Mate; Juan Trujillo 001; Jorge García 003 (DOLAP, 2022)

Law Modeling for Fairness Requirements Elicitation in Artificial Intelligence Systems. - Ana Lavalle; Alejandro Maté; Juan Trujillo 001; Jorge García-Carrasco (ER, 2022)

A Data-Driven Methodology for Guiding the Selection of Preprocessing Techniques in a Machine Learning Pipeline. - Jorge García-Carrasco; Alejandro Maté; Juan Trujillo 001 (CAiSE Forum, 2023)

Feature engineering of EEG applied to mental disorders: a systematic mapping study. - Sandra García-Ponsoda; Jorge García-Carrasco; Miguel A. Teruel; Alejandro Maté; Juan Trujillo 001 (Appl. Intell., 2023)

How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability. - Jorge García-Carrasco; Alejandro Maté; Juan C. Trujillo (AISTATS, 2024)

Detecting and Understanding Vulnerabilities in Language Models via Mechanistic Interpretability. - Jorge García-Carrasco; Alejandro Maté; Juan Trujillo 001 (IJCAI, 2024)

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference. - Jorge García-Carrasco; Alejandro Maté; Juan Trujillo 001 (AAAI, 2025)

Enabling the Application of Graph Neural Networks on Graphs With Unknown Connectivity. - Jorge García-Carrasco; Alejandro Maté; Juan Trujillo 001 (Expert Syst. J. Knowl. Eng., 2025)