Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

翻译：可解释人工智能（XAI）已成为AI驱动交互系统的重要组成部分。随着增强现实（AR）日益融入日常生活，XAI在AR中的作用也变得至关重要，因为最终用户将频繁与智能服务交互。然而，如何在AR中设计有效的XAI体验尚不明确。我们提出XAIR，一个解决在AR中“何时”、“什么”以及“如何”提供AI输出解释的设计框架。该框架基于对XAI和人机交互研究的多学科文献综述、一项调查500多名最终用户对AR解释偏好的大规模调研，以及三次与12名专家收集关于AR中XAI设计见解的工作坊。通过一项与10名设计师的研究和另一项与12名最终用户的研究验证了XAIR的实用性和有效性。XAIR可为设计师提供指导，激发他们发现新的设计机会，并在AR中实现有效的XAI设计。