Towards a Grounded Dialog Model for Explainable Artificial Intelligence
This work addresses the need for trust in XAI systems by improving explanation models for users, but it appears incremental as it builds on existing conceptual models.
The authors tackled the challenge of meaningful interaction in Explainable AI by analyzing 398 explanation dialog transcripts to propose a human explanation dialog model, resulting in a generalized state model derived from grounded theory analysis.
To generate trust with their users, Explainable Artificial Intelligence (XAI) systems need to include an explanation model that can communicate the internal decisions, behaviours and actions to the interacting humans. Successful explanation involves both cognitive and social processes. In this paper we focus on the challenge of meaningful interaction between an explainer and an explainee and investigate the structural aspects of an explanation in order to propose a human explanation dialog model. We follow a bottom-up approach to derive the model by analysing transcripts of 398 different explanation dialog types. We use grounded theory to code and identify key components of which an explanation dialog consists. We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog. We present a generalized state model obtained by the analysis and compare it with an existing conceptual dialog model of explanation.