35 citations · 88 across the 10 of their papers we have counts for
10 papers
A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption
Pedro A. Moreno-Sánchez, Javier Del Ser, Mark van Gils +1
Artificial Intelligence (AI) holds great promise for transforming healthcare, particularly in disease diagnosis, prognosis, and patient care. The increasing availability of digital…
World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child
Javier Del Ser, Jesus L. Lobo, Heimo Müller +1
World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making. While widely used in reinforcement learning, they lack the…
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
Alain Andres, Aitor Martinez-Seras, Ibai Laña +1
In the realm of human-machine interaction, artificial intelligence has become a powerful tool for accelerating data modeling tasks. Object detection methods have achieved outstandi…
On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data
Aitor Martinez-Seras, Javier Del Ser, Aitzol Olivares-Rad +2
Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capabilit…
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization
Erik B. Terres-Escudero, Javier Del Ser, Pablo Garcia-Bringas
Forward-only learning algorithms have recently gained attention as alternatives to gradient backpropagation, replacing the backward step of this latter solver with an additional co…
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh +3
Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it…
Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
Ibai Laña, Ignacio, Olabarrieta +1
The estimation of the amount of uncertainty featured by predictive machine learning models has acquired a great momentum in recent years. Uncertainty estimation provides the user w…
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks
Aitor Martinez Seras, Javier Del Ser, Jesus L. Lobo +2
Research around Spiking Neural Networks has ignited during the last years due to their advantages when compared to traditional neural networks, including their efficient processing…
Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques
Dušan Fister, Jorge Pérez-Aracil, César Peláez-Rodríguez +2
In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction te…
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Xiaodan Xing, Huanjun Wu, Lichao Wang +5
Data quality is the key factor for the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can help improve the accu…