对可解释的人工智能应用程序以人为中心的评估:系统性审查
Jenia Kim1, Henry Maathuis1,2, Danielle Sent1,2
1HU University of Applied Sciences Utrecht, Research Group Artificial Intelligence, Utrecht, Netherlands.
Frontiers in artificial intelligence
|November 1, 2024
概括
这项研究回顾了用户如何评估可解释的人工智能 (XAI). 它确定了30个有意义解释的组成部分,并呼吁在XAI研究中使用标准化的用户评估方法.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 可解释的人工智能 (XAI) 旨在消除AI系统的神秘性.
- 可解释性越来越被认为是一种以人为中心的品质.
- 目前的研究缺乏关于XAI.用户评估的共识.
研究的目的:
- 系统地审查和综合以人为中心的XAI评估的状态.
- 识别使人工智能解释对用户有意义的组件.
- 提出一个从用户角度评估XAI的分类学.
主要方法:
- 对73篇关于用户评估XAI的论文进行系统文献审查.
- 对有助于解释意义的组件进行分析.
- 将评估组件分类为一个分类学.
主要成果:
- 确定了30个有意义解释的组成部分.
- 开发了一个基于解释质量,人-人工智能交互和性能的分类学.
- 在XAI用户研究中发现方法标准化显著缺乏.
结论:
- 了解用户的感知是有效XAI的关键.
- 需要对XAI用户评估采取统一的方法.
- 标准化的方法将能够更好地进行交叉研究比较,并推进XAI研究.
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