544 citations · 557 across the 8 of their papers we have counts for
10 papers
Governing Artificial Intelligence as a Macro Socio-Technical Regime: The F-I-C-P Framework for Regime-Level AI Infrastructure and Governance
Francisco Herrera
Artificial intelligence (AI) is often analysed through technical, organisational, or regulatory perspectives that treat capability, adoption, and governance as separate issues. Thi…
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-Díaz, 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…
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…
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…
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…
Opacity as a Feature, Not a Flaw: The LoBOX Governance Ethic for Role-Sensitive Explainability and Institutional Trust in AI
Francisco Herrera, Reyes Calderón
This paper introduces LoBOX (Lack of Belief: Opacity \& eXplainability) governance ethic structured framework for managing artificial intelligence (AI) opacity when full transparen…
Aligning Trustworthy AI with Democracy: A Dual Taxonomy of Opportunities and Risks
Oier Mentxaka, Natalia Díaz-Rodríguez, 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…
Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction
José Daniel Pascual-Triana, Alberto Fernández, Paulo Novais +1
One of the key issues regarding classification problems in Trustworthy Artificial Intelligence is ensuring Fairness in the prediction of different classes when protected (sensitive…
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
Luca Longo, Mario Brcic, Federico Cabitza +16
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In respo…