可解释的人-工作建议:挑战,方法和比较分析.
Fang Tang1, Renqi Zhu1, Feng Yao1
1School of Systems Engineering, National University of Defense Technology, Changsha, China.
Frontiers in artificial intelligence
|October 27, 2025
概括
在人与工作推系统 (PJRS) 中,可解释AI (XAI) 对透明度至关重要. 反事实方法提供了最佳的可解释性-性能平衡,增强了对招聘决策的信任.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 信息科学 信息科学 信息科学
背景情况:
- 人与工作推系统 (PJRS) 越来越多地用于招聘,引发了人们对其"黑子"性质的担忧.
- 对可解释AI (XAI) 解决方案的需求正在增长,以解决PJRS的不透明性.
- 本审查系统地检查了PJRS应用的XAI方法.
研究的目的:
- 对PJRS的XAI技术进行审查和分类.
- 为透明的PJRS制定一个综合框架.
- 提供不同XAI方法的性能-可解释性权衡的比较证据.
主要方法:
- 从主要学术数据库中对85项研究 (2019-2025) 进行系统审查,遵循PRISMA 2020指南.
- 将可解释性技术分类为数据,模型和输出层.
- 六种代表性的XAI方法的定量基准测试.
主要成果:
- XAI技术被分为数据,模型和输出层,例如特征归属,知识图和SHAP.
- 一个集成的端到端框架被合成来解决所有层的不透明性.
- 反事实方法获得了最高的可解释性-性能 (E-P) 评分0.95.
结论:
- 该审查提供了分类学,跨层框架和经验数据,以指导可信的PJRS的开发.
- 未来的研究应该专注于多式联络因果推断,适应性系统和高效的解释性工具.
- 这项工作支持创建更加透明和可靠的人工智能驱动的招聘系统.
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