Towards a Grounded Dialog Model for Explainable Artificial Intelligence

TL;DR

This paper develops a grounded dialog model for explainable AI by analyzing real explanation conversations, aiming to improve human-AI interactions through structured explanation processes.

Contribution

It introduces a novel explanation dialog model derived from empirical analysis of transcripts, enhancing understanding of human-AI explanation interactions.

Findings

Identified key components of explanation dialogs

Mapped relationships and sequences in explanation conversations

Compared the model with existing explanation dialog frameworks

Abstract

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.