提出三维度框架，系统分类可解释AI研究以解决领域混乱问题
Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks
- 从‘做什么、为什么做、谁来做’三方面构建分类框架
- 指出现有研究普遍存在任务描述不清、脱离使用场景等问题
- 为可解释AI设计提供报告指南，帮助识别研究空白与矛盾结论
可解释人工智能（XAI）在数据分析任务中的研究存在大量矛盾，且缺乏具体设计建议，根源在于对需AI辅助任务的理解不足。本文综合视觉分析、认知科学与仪表板设计等领域，提出一个基于‘什么、为什么、谁’三个维度的XAI研究分类与比较方法。研究发现主要问题包括：任务描述不充分、研究脱离实际情境、未针对目标用户进行验证。我们建议研究应明确报告用户在领域知识、AI理解及数据分析能力方面的水平，以说明研究结果的普适性。同时提出XAI任务设计与报告的指导原则，旨在提升该领域快速发展的可读性与可比性。期望本工作能帮助研究人员与设计者更准确地判断哪些研究相关、存在哪些缺口，以及如何应对关于XAI设计的矛盾结论。
原文摘要 · Abstract (English)
Research into explainable artificial intelligence (XAI) for data analysis tasks suffer from a large number of contradictions and lack of concrete design recommendations stemming from gaps in understanding the tasks that require AI assistance. In this paper, we drew on multiple fields such as visual analytics, cognition, and dashboard design to propose a method for categorising and comparing XAI studies under three dimensions: what, why, and who. We identified the main problems as: inadequate descriptions of tasks, context-free studies, and insufficient testing with target users. We propose that studies should specifically report on their users' domain, AI, and data analysis expertise to illustrate the generalisability of their findings. We also propose study guidelines for designing and reporting XAI tasks to improve the XAI community's ability to parse the rapidly growing field. We hope that our contribution can help researchers and designers better identify which studies are most relevant to their work, what gaps exist in the research, and how to handle contradictory results regarding XAI design.
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