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Computer Science > Human-Computer Interaction

arXiv:2303.16292 (cs)
[Submitted on 28 Mar 2023]

Title:XAIR: A Framework of Explainable AI in Augmented Reality

Authors: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.
Comments: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
MSC classes: 68U35
ACM classes: H.5.2; I.2.m
Cite as: arXiv:2303.16292 [cs.HC]
  (or arXiv:2303.16292v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2303.16292
Related DOI: https://doi.org/10.1145/3544548.3581500

Submission history

From: Xuhai Xu [view email]
[v1] Tue, 28 Mar 2023 20:14:29 UTC (39,623 KB)
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