Diagnosing AI Explanation Methods with Folk Concepts of Behavior
This work addresses the challenge of making AI explanations more effective for human users, though it is incremental as it builds on existing theory of mind literature.
The paper tackles the problem of evaluating AI explanation methods by proposing a framework based on folk concepts of behavior to assess what information humans understand from explanations, and it identifies failure modes in current methods that could lead to misunderstandings.
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.