Explainable Artificial Intelligence: An Updated Perspective

Abstract

Artificial intelligence has become mainstream and its applications will only proliferate. Specific measures must be done to integrate such systems into society for the general benefit. One of the tools for improving that is explainability which boosts trust and understanding of decisions between humans and machines. This research offers an update on the current state of explainable AI (XAI). Recent XAI surveys in supervised learning show convergence of main conceptual ideas. We list the applications of XAI in the real world with concrete impact. The list is short and we call to action - to validate all the hard work done in the field with applications that go beyond experiments on datasets, but drive decisions and changes. We identify new frontiers of research, explainability of reinforcement learning and graph neural networks. For the latter, we give a detailed overview of the field.

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