Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
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可解释人工智能（XAI）：迈向负责任人工智能的概念、分类、机遇与挑战
DOI：
10.1016/j.inffus.2019.12.012
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发表时间：
2020-06-01
影响因子：
18.6
通讯作者：
Herrera, Francisco
中科院分区：
文献类型：
--
作者：
Barredo Arrieta, Alejandro;Diaz-Rodriguez, Natalia;Herrera, Francisco
在过去的几年里，人工智能(AI)取得了显著的发展势头，如果利用得当，可能会在该领域的许多应用领域提供最佳的预期。对于机器学习来说，这很快就会发生，整个社区都站在可解释性的障碍面前，这是子符号主义(例如集成或深度神经网络)带来的最新技术的固有问题，而这些技术在上一次人工智能的炒作(即专家系统和基于规则的模型)中没有出现。这个问题背后的范式属于所谓的可解释人工智能(XAI)领域，这被广泛认为是人工智能模型实际部署的关键特征。本文概述了现有的文献和已有的研究成果，并对尚未达到的研究成果进行了展望。为此，我们总结了以往在机器学习中定义可解释性的努力，建立了可解释机器学习的新定义，该定义涵盖了这些先前的概念命题，主要关注寻求可解释性的受众。从这一定义出发，我们提出并讨论了与不同机器学习模型的解释性有关的最近贡献的分类，包括那些旨在解释深度学习方法的分类，为其建立了第二个专门的分类，并进行了详细的检查。这一批判性的文献分析为Xai面临的一系列挑战提供了激励背景，例如数据融合和可解释性的有趣十字路口。我们的前景导致了负责任人工智能的概念，即一种在真实组织中大规模实施人工智能方法的方法，其核心是公平、模型可解释性和问责制。我们的最终目标是为XAI领域的新手提供一个全面的分类学，可以作为参考材料，以刺激未来的研究进展，但也是为了鼓励其他学科的专家和专业人员在他们的活动部门接受人工智能的好处，而不是因为它缺乏解释性而事先存在任何偏见。
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.