Diagnosing AI Explanation Methods with Folk Concepts of Behavior

TL;DR

This paper proposes a formal framework using folk concepts of behavior to evaluate and improve AI explanation methods, aiming to enhance human understanding and reduce misunderstandings.

Contribution

It introduces a novel folk concept-based framework for diagnosing AI explanation methods and maps existing methods to this framework for qualitative evaluation.

Findings

Many current XAI methods can be mapped to folk concepts of behavior

Identifies failure modes in explanation methods related to missing information constructs

Including missing constructs can improve explanation effectiveness

Abstract

We investigate a formalism for the conditions of a successful explanation of AI. We consider "success" to depend not only on what information the explanation contains, but also on what information the human explainee understands from it. Theory of mind literature discusses the folk concepts that humans use to understand and generalize behavior. We posit that folk concepts of behavior provide us with a "language" that humans understand behavior with. We use these folk concepts as a framework of social attribution by the human explainee - the information constructs that humans are likely to comprehend from explanations - by introducing a blueprint for an explanatory narrative (Figure 1) that explains AI behavior with these constructs. We then demonstrate that many XAI methods today can be mapped to folk concepts of behavior in a qualitative evaluation. This allows us to uncover their failure modes that prevent current methods from explaining successfully - i.e., the information constructs that are missing for any given XAI method, and whose inclusion can decrease the likelihood of misunderstanding AI behavior.