A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
复制标题
DOI：
10.1145/3387166
复制
发表时间：
2021-08-01
影响因子：
3.4
通讯作者：
Ragan, Eric D.
中科院分区：
文献类型：
--
作者：
Mohseni, Sina;Zarei, Niloofar;Ragan, Eric D.
对日常生活中使用的人工智能（AI）应用的普遍性，对可解释和负责的智能系统的需求也在增长。可解释的AI（XAI）系统旨在自我解释系统决策和预测背后的推理。来自不同学科的研究人员共同努力定义，设计和评估可解释的系统。但是，来自不同学科的学者集中在XAI研究的不同目标和相当独立的主题上，这对确定适当的设计和评估方法和整合知识的挑战提出了挑战。为此，本文提出了一个调查和框架，旨在分享跨多个学科的XAI设计和评估方法的知识和经验。旨在支持XAI研究中的各种设计目标和评估方法，在对机器学习，可视化和人类计算机互动领域的XAI相关论文进行了详尽的回顾之后，我们提出了XAI设计目标和评估方法的分类。我们的分类介绍了不同XAI用户组的设计目标及其评估方法之间的映射。从我们的发现中，我们开发了一个框架，其中包括分步设计指南，并配对评估方法，以关闭多学科XAI团队中的迭代设计和评估周期。此外，我们为XAI研究中不同目标的评估方法和建议提供了总结的现成表。
The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI (XAI) systems are intended to selfexplain the reasoning behind system decisions and predictions. Researchers from different disciplines work together to define, design, and evaluate explainable systems. However, scholars from different disciplines focus on different objectives and fairly independent topics of XAI research, which poses challenges for identifying appropriate design and evaluation methodology and consolidating knowledge across efforts. To this end, this article presents a survey and framework intended to share knowledge and experiences of XAI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation methods in XAI research, after a thorough review of XAI related papers in the fields of machine learning, visualization, and human-computer interaction, we present a categorization of XAI design goals and evaluation methods. Our categorization presents the mapping between design goals for different XAI user groups and their evaluation methods. From our findings, we develop a framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation cycles in multidisciplinary XAI teams. Further, we provide summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research.