对于开放知识提取系统的自动透明度评估
Maryam Basereh1, Annalina Caputo2, Rob Brennan3
1School of Computing, Dublin City University, Dublin, Ireland. maryam.basereh@adaptcentre.ie.
Journal of biomedical semantics
|August 31, 2023
概括
本研究介绍了Cyrus,这是开放知识提取 (OKE) 系统的透明度评估框架. 赛拉斯量化了OKE系统的透明度,揭示了链接数据质量的差异,并帮助可信的人工智能开发.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 信息科学 信息科学 信息科学
背景情况:
- 开放式知识提取 (OKE) 系统对于知识服务至关重要,但往往缺乏透明度,使其流程和结果难以理解.
- 欧盟强调值得信赖的人工智能 (AI) 以及人工智能系统需要透明度.
- 现有的透明度模型和相关的数据质量评估是分散的,需要为OKE系统提供统一的框架.
研究的目的:
- 提出Cyrus,一个专门为开放知识提取 (OKE) 系统设计的新型透明度评估框架.
- 通过整合来自FAccT,FAIR和链接数据质量研究的观点,提供透明度维度的全面视图.
- 实现OKE系统的自动化透明度评估,促进比较和改进.
主要方法:
- 开发了基于最先进的透明度模型和链接数据质量维度的Cyrus框架.
- 评估了使用Cyrus框架的最先进OKE系统生成的三个链接数据集的透明度.
- 使用自动化评估,结合公平性评估工具和Luzzu链接数据质量框架来评估Cyrus六个透明度维度.
主要成果:
- 赛拉斯框架成功评估了三个OKE系统在六个关键维度上的透明度:来源,可解释性,可理解性,许可,可用性和相互连接.
- 在被评估的OKE系统中,Covid-on-the-Web显示了最高的平均透明度.
- 在最先进的OKE系统中实现了透明度差异的自动量化.
结论:
- 塞鲁斯为OKE系统提供了第一个全面的透明度框架,将道德,可信的人工智能和数据质量观点融合在一起.
- 通过整合现有的公平性和数据质量评估工具,对OKE系统的自动化透明度评估是可行的.
- 该研究表明,OKE系统的透明度有所不同,可以自动量化,这对可信的人工智能,合规性和系统设计都有影响.
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