评估可解释AI的模型要求:一个模板和示例案例研究
Michael Heider1, Helena Stegherr2, Richard Nordsieck3
1Universität Augsburg, Organic Computing Group. michael.heider@uni-a.de.
Artificial life
|August 23, 2023
概括
社会技术环境中的决策支持系统 (DSS) 可以通过学习分类系统 (LCS) 提高透明度. 使用七个问题的一种新方法有助于设计LCS模型,以便更好地实现人与人工智能的交互.
科学领域:
- 社会技术系统 社会技术系统
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 人类操作员越来越多地在社会技术环境中使用决策支持系统 (DSS).
- 有效的DSS需要透明的决策,以便运营商接受和参与.
- 自适应和自优化是DSS有望改善的关键特性.
研究的目的:
- 提出学习分类器系统 (LCSs) 在DSS中进行透明的决策.
- 在LCS设计中引入一种新的方法来评估特定应用程序的可解释性需求.
- 为指导LCS模型开发提供七个问题的模板.
主要方法:
- 使用学习分类系统 (LCSs),这是一个基于规则的机器学习方法的家族.
- 开发一个独立于应用程序的七个问题的模板,以评估可解释性需求.
- 在制造场景中进行基于采访的案例研究.
主要成果:
- 拟议的七个问题模板为LCS模型设计提供了有用的见解.
- 确定了利益相关方对智能代理人积极参与的需求.
- 该方法有助于设计透明和可解释的LCS模型.
结论:
- 学习分类系统 (LCS) 提供了一种可行的方法来提高决策支持系统的透明度.
- 评估可解释性需求的结构化方法对于有效的LCS设计至关重要.
- 在LCS中提高透明度促进了人类操作员和智能代理之间的更好的协作.
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