Introduction to Explainable AI
Introduction to Explainable AI
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可解释人工智能简介
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
R. Bellamy
中科院分区：
文献类型：
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作者：
Q. Liao;Moninder Singh;Yunfeng Zhang;R. Bellamy
随着人工智能(AI)技术越来越多地被用于做出重要决策和执行自主任务，提供解释让用户理解AI已经成为人类与AI交互中普遍关注的问题。最近，一些开源工具包正在使越来越多的可解释人工智能(XAI)技术可供研究人员和从业者使用，以将解释功能纳入人工智能系统。本课程向任何对XAI主题的实施、设计和研究感兴趣的人开放，旨在提供关于XAI的趋势和方法的概述，并帮助学员获得使用XAI工具包创建不同风格的解释的实践经验。
As Artificial Intelligence (AI) technologies are increasingly used to make important decisions and perform autonomous tasks, providing explanations that allow users to understand the AI has become a ubiquitous concern in human-AI interaction. Recently, a number of open-source toolkits are making the growing collection of of Explainable AI (XAI) techniques accessible for researchers and practitioners to incorporate explanation features in AI systems. This course is open to anyone interested in implementing, designing and researching on the topic of XAI, aiming to provide an overview on the trends and methods of XAI and also help attendees gain hands-on experience of creating different styles of explanation with an XAI toolkit.