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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
TLDR
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.Abstract:
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.read more
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Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
TL;DR: Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made as mentioned in this paper , which is particularly true of the most popular deep neural network approaches currently in use.
Journal ArticleDOI
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications
Juan Manuel Górriz,Juan Manuel Górriz,Javier Ramírez,Andrés Ortiz,Francisco Jesús Martínez-Murcia,Fermín Segovia,John Suckling,Matthew Leming,Yudong Zhang,José Ramón Álvarez-Sánchez,Guido Bologna,Paula Bonomini,Fernando E. Casado,David Charte,Francisco Charte,Ricardo Contreras,Alfredo Cuesta-Infante,Richard J. Duro,Antonio Fernández-Caballero,Eduardo Fernández-Jover,Pedro Gómez-Vilda,Manuel Graña,Francisco Herrera,Roberto Iglesias,Anna Lekova,Javier de Lope,Ezequiel López-Rubio,Rafael Martínez-Tomás,Miguel A. Molina-Cabello,Antonio S. Montemayor,Paulo Novais,Daniel Palacios-Alonso,Juan José Pantrigo,Bryson R. Payne,Félix de la Paz López,María Angélica Pinninghoff,Mariano Rincón,José Santos,Karl Thurnhofer-Hemsi,Athanasios Tsanas,Ramiro Varela,José Manuel Ferrández +41 more
TL;DR: A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence.
Journal ArticleDOI
Explainability in deep reinforcement learning
TL;DR: A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data as mentioned in this paper.
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