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arXiv · 2303.16292

XAIR: A Framework of Explainable AI in Augmented Reality

Xuhai Xu·Mengjie Yu·Tanya R. Jonker·Kashyap Todi·Feiyu Lu·Xun Qian·João Marcelo Evangelista Belo·Tianyi Wang·Michelle Li·Aran Mun·Te-Yen Wu·Junxiao Shen·Ting Zhang·Narine Kokhlikyan·Fulton Wang·Paul Sorenson·Sophie Kahyun Kim·Hrvoje Benko
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Abstract

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.

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Xuhai Xu, Mengjie Yu, Tanya R. Jonker, Kashyap Todi, Feiyu Lu, Xun Qian, João Marcelo Evangelista Belo, Tianyi Wang, Michelle Li, Aran Mun, Te-Yen Wu, Junxiao Shen, Ting Zhang, Narine Kokhlikyan, Fulton Wang, Paul Sorenson, Sophie Kahyun Kim, Hrvoje Benko. 2023-03-28. XAIR: A Framework of Explainable AI in Augmented Reality. https://doi.org/10.1145/3544548.3581500

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