This paper provides a brief analytical review of the current state-of-the-art in relation to the explainability of artificial intelligence in the context of recent advances in machine learning and deep learning. The paper starts with a brief historical introduction and a taxonomy, and formulates the main challenges in terms of explainability building on the recently formulated National Institute of Standards four principles of explainability. Recently published methods related to the topic are then critically reviewed and analyzed. Finally, future directions for research are suggested.

中文翻译：

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可解释的人工智能：分析综述

在机器学习和深度学习的最新进展背景下，本文简要分析了当前与人工智能可解释性相关的最新技术。本文从简要的历史介绍和分类开始，并根据美国国家标准学会最近制定的可解释性四项原则阐述了可解释性方面的主要挑战。然后对最近发表的与该主题相关的方法进行批判性审查和分析。最后，提出了未来的研究方向。