来自对可解释AI算法的可理解性进行用户研究的数据集
Szymon Bobek1, Paloma Korycińska2, Monika Krakowska2
1Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research, Institute of Applied Computer Science, Jagiellonian University, Krakow, Poland. szymon.bobek@uj.edu.pl.
Scientific data
|June 13, 2025
概括
这项研究引入了一个关于可解释AI (XAI) 算法可理解性的新型数据集. 它提供了来自不同参与者的采访成绩单,使人工智能解释能够进行多学科分析.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 数据科学数据科学数据科学
背景情况:
- 可解释的人工智能 (XAI) 正在迅速发展.
- 对XAI可理解性的多学科评估至关重要.
- 现有的数据集缺乏关于XAI解释的定性,以用户为中心的数据.
研究的目的:
- 介绍一个关于XAI算法可理解性的用户研究的综合数据集.
- 在XAI中促进可重现的研究和进一步分析.
- 能够研究不同用户群体如何理解AI决策.
主要方法:
- 招募了149名参与者,组成了三个不同的组:真菌学领域专家 (DE),信息技术学生 (IT) 和社会科学和人文学科学生 (SSH).
- 收集了39份采访成绩单,详细介绍了用户对人工智能解释分类的解释.
- 通过可视化,主题分析,用户建议和研究前关于领域知识和数据素养的调查来补充成绩单.
主要成果:
- 数据集包括与可视化和分析结果相关的手动标记的成绩单.
- 参与者数据涵盖了一系列领域的专业知识和数据分析素养.
- 该数据集支持对用户与XAI.AI互动的详细定性分析.
结论:
- 开发的数据集为评估XAI在不同用户群体的可理解性提供了一个独特的资源.
- 能够进一步研究影响人工智能理解的人类因素.
- 支持以用户为中心的XAI设计和评估的进步.
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