Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Abstract

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of 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 the field of XAI with a thorough taxonomy that can serve as 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.

A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

“…As we stated before, the interpretability and the explainability constitute key factors which are of high significance in ML practical models. Interpretability is the ability of understanding and observing a model's mechanism or prediction behavior depending on its input stimulation while, explainability is the ability to demonstrate and explain in understandable terms to a human, the models' decision on predictions problems [7,9]. There are a lot of tasks where the prediction accuracy of a ML model is not the only important issue but, equally important is the understanding, the explanation and the presentation of the model's decision [8].…”

“…In general, there are two main categories of techniques which provide interpretability in machine learning. These are intrinsic interpretability and post-hoc interpretability [7,14]. Intrinsic interpretability is acquired by developing prediction models which are by their nature interpretable, such as all the White-Box models.…”

“…In general, a classification model must be accurate in its predictions. However, another significant factor in ML models is the explainability and the interpretability of classifiers' predictions [5][6][7]. There are many real-world tasks which place significant importance in the understanding and the explanation of the model's prediction [8], especially in crucial and decisive decisions such as cancer diagnosis.…”

A Survey on Explainability: Why Should We Believe the Accuracy of A Model?

“…According to Tim Miller [33], Explainable Artificial Intelligence (XAI) refers to "an explanatory agent revealing underlying causes to its or another agent's decision making. " Recently Arrieta et al [4] have also defined it as "Given an audience an explainable Artificial Intelligence is one that produces details or reasons to make its functioning clear to understand. "…”

“…In a very recent and elaborate survey published in 2019, by Arrieta et al [4], the authors discuss the rising trend of explainability in Machine Learning. According to the authors, explainability could be achieved successfully either through models that are transparent, i.e.…”