关于黑子决策的以利益相关者为中心的解释:XAI流程模型及其应用于汽车行业的商誉评估
Stefan Haas1,2, Konstantin Hegestweiler1,2, Michael Rapp1
1Institute of Informatics, LMU Munich, Munich, Germany.
Frontiers in artificial intelligence
|November 8, 2024
概括
可解释的人工智能增强了对关键应用程序机器学习模型的信任. 基于本地特征重要性的文本解释最能满足利益相关者的需求,在企业环境中越来越受欢迎.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 高性能机器学习模型往往不透明,阻碍了高风险领域的信任和采用.
- 缺乏模型透明度挑战了人类专家对人工智能驱动的决策支持系统的接受.
研究的目的:
- 提出和评估一个流程模型,用于开发和评估可解释的,针对不同利益相关者量身定制的决策支持系统.
- 为了提高黑盒机器学习模型的接受度,用于汽车行业的商誉评估.
主要方法:
- 为可解释AI (XAI) 开发和应用一个过程模型.
- 与各种利益相关者 (商业专家,评估员,IT专家) 进行定量调查.
- 评估基于忠实性和稳定性的解释方法.
主要成果:
- 根据当地特征的重要性进行的文本解释被认为是最适合利益相关者.
- 所有受访的利益相关者都报告说,当他们得到这些解释时,他们对决策支持系统的信任增加了.
- 选择的解释方法在技术评估中表现出了可靠性和稳定性.
结论:
- 拟议的过程模型有助于在企业环境中部署可靠的机器学习.
- 根据特定利益相关者的需求量身定制解释对于成功采用人工智能至关重要.
- 可解释的人工智能显著提高了对人工智能系统在现实世界应用中的信心和接受.
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