  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.
