7 papers
DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
J. Javier Alonso-Ramos, Ignacio Aguilera-Martos, Francisco Herrera +1
In the Data-Centric Artificial Intelligence (AI) paradigm, improving data quality is essential for robust machine learning. However, many denoising methods rely on rigid statistica…
SeNeDiF-OOD: Semantic Nested Dichotomy Fusion for Out-of-Distribution Detection Methodology in Open-World Classification. A Case Study on Monument Style Classification
Ignacio Antequera-Sánchez, Juan Luis Suárez-DÃaz, Rosana Montes +1
Out-of-distribution (OOD) detection is a fundamental requirement for the reliable deployment of artificial intelligence applications in open-world environments. However, addressing…
Data Science: a Natural Ecosystem
Emilio Porcu, Roy El Moukari, Laurent Najman +2
This manuscript provides a systemic and data-centric view of what we term essential data science, as a natural ecosystem with challenges and missions stemming from the fusion of da…
Aligning Trustworthy AI with Democracy: A Dual Taxonomy of Opportunities and Risks
Oier Mentxaka, Natalia DÃaz-RodrÃguez, Mark Coeckelbergh +5
Artificial Intelligence (AI) poses both significant risks and valuable opportunities for democratic governance. This paper introduces a dual taxonomy to evaluate AI's complex relat…
A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance
Andrés Herrera-Poyatos, Javier Del Ser, Marcos López de Prado +3
Responsible Artificial Intelligence (RAI) addresses the ethical and regulatory challenges of deploying AI systems in high-risk scenarios. This paper proposes a comprehensive framew…
Making Sense of the Unsensible: Reflection, Survey, and Challenges for XAI in Large Language Models Toward Human-Centered AI
Francisco Herrera
As large language models (LLMs) are increasingly deployed in sensitive domains such as healthcare, law, and education, the demand for transparent, interpretable, and accountable AI…