梳理解释性界面设计方法，帮AI系统更易懂、更好用。
Explanation User Interfaces: A Systematic Literature Review
- 系统综述现有解释界面设计方法与原则
- 提出支持人类中心设计的实践平台HERMES
- 适合关注AI可解释性的研究人员和开发者
人工智能是本世纪最重要的技术进步之一，在众多领域通过智能应用展现出巨大潜力。然而，由于其决策过程常为黑箱（即难以理解），开发者通常借助可解释人工智能（XAI）技术来解析模型行为，以构建透明、公平、可靠且可信的系统。但如何向用户有效呈现解释却非易事，往往被忽视，导致最终系统对终端用户无实际帮助。本文开展了一项关于解释性用户界面（XUIs）的系统文献综述，旨在深入理解学术界用于向用户有效呈现解释的解决方案与设计指南。为提升研究贡献与现实影响力，本文还提出了一个支持人类中心化可解释界面开发的平台HERMES，为研究者与实践者提供设计与评估XUIs的指导。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behaviour of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system's design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a platform to support Human-cEnteRed developMent of Explainable user interfaceS (HERMES) and guide practitioners and scholars in the design and evaluation of XUIs.
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