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Alberto Barbado
Researcher at Telefónica
Publications - 13
Citations - 4499
Alberto Barbado is an academic researcher from Telefónica. The author has contributed to research in topics: Computer science & Fuel efficiency. The author has an hindex of 5, co-authored 13 publications receiving 1240 citations. Previous affiliations of Alberto Barbado include Technical University of Madrid & National University of Distance Education.
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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.
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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: 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.
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Responsible AI by Design in Practice
TL;DR: This paper discusses the practical case of a large organization that is putting in place a company-wide methodology to minimize the risk of undesired consequences of AI.
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Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM
TL;DR: This paper evaluates some of the most important rule extraction techniques over OneClass SVM models, as well as presenting alternative designs for some of those XAI algorithms, and proposes algorithms to compute metrics related with XAI regarding the "comprehensivility", "representativeness", "stability" and "diversity" of the rules extracted.
Journal ArticleDOI
Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM
TL;DR: In this article, the authors evaluate several rule extraction techniques over one-class SVM models and propose algorithms for computing metrics related to eXplainable Artificial Intelligence (XAI) regarding the "comprehensibility", "representativeness", "stability" and "diversity" of the extracted rules.