LEx：一个操作化机器学习解释层的框架
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
摘要：
有几个社会因素会影响人们对人工智能解释的反应，这些解释用于证明影响他们个人的人工智能决策。在这份立场文件中，我们定义了一个称为\textit{layers of explanation}（LEx）的框架，通过这个框架，我们可以评估不同类型解释的适当性。该框架使用的概念\textit{sensitivity}（情绪反应）的功能和\textit{stakes}（决策的后果）在一个域中，以确定是否不同类型的解释是\textit{appropriate}在给定的上下文中。我们展示了如何使用该框架来评估不同领域中不同类型解释的适当性。
英文摘要：
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the \textit{layers of explanation} (LEx), a lens through which we can assess the appropriateness of different types of explanations. The framework uses the notions of \textit{sensitivity} (emotional responsiveness) of features and the level of \textit{stakes} (decision's consequence) in a domain to determine whether different types of explanations are \textit{appropriate} in a given context. We demonstrate how to use the framework to assess the appropriateness of different types of explanations in different domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
