Towards Trustable Explainable AI
Towards Trustable Explainable AI
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DOI：
10.24963/ijcai.2020/726
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Alexey Ignatiev
中科院分区：
文献类型：
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作者：
Alexey Ignatiev
可解释的人工智能（XAI）可以说是当今AI领域面临的最关键的挑战之一。尽管大多数XAI方法都是具有启发式性质的，但最近的工作提出了将绑架推理的使用用于计算机器学习（ML）预测的正确解释。所提出的严格方法不仅可用于计算可信赖的解释，而且对于验证计算启发式的解释。它也用于发现XAI与ML模型验证之间的密切关系。本文概述了严格的基于逻辑的XAI方法的进步，并认为如果令人信服的XAI是必不可少的。
Explainable artificial intelligence (XAI) represents arguably one of the most crucial challenges being faced by the area of AI these days. Although the majority of approaches to XAI are of heuristic nature, recent work proposed the use of abductive reasoning to computing provably correct explanations for machine learning (ML) predictions. The proposed rigorous approach was shown to be useful not only for computing trustable explanations but also for validating explanations computed heuristically. It was also applied to uncover a close relationship between XAI and verification of ML models. This paper overviews the advances of the rigorous logic-based approach to XAI and argues that it is indispensable if trustable XAI is of concern.