J
Javier Del Ser
Researcher at University of the Basque Country
Publications - 346
Citations - 10301
Javier Del Ser is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 27, co-authored 281 publications receiving 4412 citations. Previous affiliations of Javier Del Ser include Basque Center for Applied Mathematics & Centro de Estudios e Investigaciones Técnicas de Gipuzkoa.
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
Posted Content
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: Previous efforts to define explainability in Machine Learning are summarized, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought, and a taxonomy of recent contributions related to the explainability of different Machine Learning models are proposed.
Journal ArticleDOI
Bio-inspired computation: Where we stand and what's next
Javier Del Ser,Javier Del Ser,Eneko Osaba,Daniel Molina,Xin-She Yang,Sancho Salcedo-Sanz,David Camacho,Swagatam Das,Ponnuthurai Nagaratnam Suganthan,Carlos A. Coello Coello,Francisco Herrera +10 more
TL;DR: The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
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Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
TL;DR: The so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of the global economy.
Journal ArticleDOI
Road Traffic Forecasting: Recent Advances and New Challenges
TL;DR: The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.