  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.

Artificial Intelligence;Machine Learning;Neural and Evolutionary Computing