How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey

Thu Nguyen, Alessandro Canossa, Jichen Zhu
3/21/2024
cs.HCcs.AI

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Abstract

Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users. In order to improve XAI systems' usability, practical interpretability, and efficacy for real users, the emerging area of {\em Explainable Interfaces} (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development. This is among the first systematic survey of EI research.

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Cite this paper

@article{nguyen2024humancentered,
  title  = {How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey},
  author = {Thu Nguyen and Alessandro Canossa and Jichen Zhu},
  year   = {2024},
  eprint = {2403.14496},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.14496v1}
}

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