Explanations as Dialogues: Toward Human-Centered Conversational Explainable AI

2026-05-26

AI Summary

This work addresses a critical limitation in current explainable artificial intelligence (XAI) research, which predominantly treats explanations as static outputs while overlooking their inherently dynamic and dialogic nature in real-world settings. To bridge this gap, the paper introduces a Human-Centered Conversational XAI (HC²XAI) framework that systematically integrates dialogue as a core dimension of XAI for the first time. Emphasizing interactivity, context dependence, and contextual awareness, HC²XAI synthesizes principles from conversational system design, human-computer interaction theory, and established XAI techniques to enable multi-turn, adaptive explanation generation. The framework’s efficacy is demonstrated through three representative application scenarios, offering a novel paradigm that shifts XAI from static explanations toward dynamic, user-responsive dialogue and opening new avenues for research within the conversational user interface (CUI) community.

Abstract

As AI systems become increasingly conversational, a gap emerges wherein explanations are studied as static artifacts, yet in practice, are experienced as dialogue. In this provocation, we argue that the conversational layer around an explanation is not incidental to its effectiveness, but a critical constituent. Drawing on three illustrative scenarios, we invite the CUI community to study explanations as interactive, conversational exchanges shaped by timing, tone, persona and conversational history, and introduce our vision for Human-Centered Conversational XAI (HC2XAI).

Problem

• Conversational AI
• Explainable AI
• Human-Centered AI
• Dialogue
• Interactive Explanation

Innovation

• Conversational XAI
• Human-Centered AI
• Explanations as Dialogues
• Interactive Explanation
• CUI