When explaining AI behavior to humans, how is the communicated information being comprehended by the human explainee, and does it match what the explanation attempted to communicate? When can we say that an explanation is explaining something? We aim to provide an answer by leveraging theory of mind literature about the folk concepts that humans use to understand behavior. We establish a framework of social attribution by the human explainee, which describes the function of explanations: the concrete information that humans comprehend from them. Specifically, effective explanations should be coherent (communicate information which generalizes to other contrast cases), complete (communicating an explicit contrast case, objective causes, and subjective causes), and interactive (surfacing and resolving contradictions to the generalization property through iterations). We demonstrate that many XAI mechanisms can be mapped to folk concepts of behavior. This allows us to uncover their modes of failure that prevent current methods from explaining effectively, and what is necessary to enable coherent explanations.

研究人工智能的解释条件，并介绍了一种“语言框架”，以人类理解行为的民俗概念作为社会归因的框架，并且将XAI方法定性地映射到行为的民间概念中，以揭示它们的失败模式，增加了解释成功的可能性。

利用民间行为概念诊断AI解释方法