Explainability in human–agent systems
作者:Avi Rosenfeld, Ariella Richardson
摘要
This paper presents a taxonomy of explainability in human–agent systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability, and its relationship to the related terms of interpretability, transparency, explicitness, and faithfulness. These definitions allow us to answer why explainability is needed in the system, whom it is geared to and what explanations can be generated to meet this need. We then consider when the user should be presented with this information. Last, we consider how objective and subjective measures can be used to evaluate the entire system. This last question is the most encompassing as it will need to evaluate all other issues regarding explainability.
论文关键词:Human–agent systems, XAI, Machine learning interpretability, Machine learning transparency
论文评审过程:
论文官网地址:https://doi.org/10.1007/s10458-019-09408-y