Methods and standards for research on explainable artificial intelligence: Lessons from intelligent tutoring systems

Applied AI letters Pub Date : 2021-11-13 DOI:10.1002/ail2.53
William J. Clancey, Robert R. Hoffman
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Abstract

The DARPA Explainable Artificial Intelligence (AI) (XAI) Program focused on generating explanations for AI programs that use machine learning techniques. This article highlights progress during the DARPA Program (2017-2021) relative to research since the 1970s in the field of intelligent tutoring systems (ITSs). ITS researchers learned a great deal about explanation that is directly relevant to XAI. We suggest opportunities for future XAI research deriving from ITS methods, and consider the challenges shared by both ITS and XAI in using AI to assist people in solving difficult problems effectively and efficiently.

Abstract Image

可解释人工智能的研究方法与标准:来自智能辅导系统的经验教训
DARPA可解释人工智能(AI) (XAI)项目专注于为使用机器学习技术的人工智能程序生成解释。本文重点介绍了DARPA计划(2017-2021)期间相对于20世纪70年代以来智能辅导系统(ITSs)领域的研究进展。ITS研究人员学到了很多与XAI直接相关的解释。我们提出了未来人工智能研究从ITS方法衍生出来的机会,并考虑了ITS和人工智能在使用人工智能帮助人们有效和高效地解决难题方面所面临的共同挑战。
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