The need for interpretable and accountable intelligent systems grows along
with the prevalence of artificial intelligence applications used in everyday
life. Explainable intelligent systems are designed to self-explain the
reasoning behind system decisions and predictions, and researchers from
different disciplines work together to define, design, and evaluate
interpretable systems. However, scholars from different disciplines focus on
different objectives and fairly independent topics of interpretable machine
learning research, which poses challenges for identifying appropriate design
and evaluation methodology and consolidating knowledge across efforts. To this
end, this paper 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 interpretable machine learning design goals and evaluation
methods to show a 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.

本文旨在分享跨学科 XP 机器学习设计和评估方法的知识和经验，并提出了 XP 智能系统设计目标和评估方法的分类以支持 XP 研究的多样性设计目标和评估方法。我们为不同 XP 用户组的设计目标和其评估方法之间的映射提供了一个分类，以及一系列的设计指南和与之相对应的评估方法，从而关闭多学科 XP 团队的迭代设计和评估循环。