Providing personalized Explanations: a Conversational Approach

TL;DR

This paper introduces a conversational method for providing personalized AI explanations, enabling tailored communication through iterative dialogue that adapts to the explainee's background and understanding.

Contribution

It proposes a novel conversational approach for personalized explanations and proves the termination of such dialogues under certain conditions.

Findings

Conversation terminates when explainee justifies initial claim

Approach adapts explanations based on explainee's background

Ensures explanations are understandable and personalized

Abstract

The increasing applications of AI systems require personalized explanations for their behaviors to various stakeholders since the stakeholders may have various knowledge and backgrounds. In general, a conversation between explainers and explainees not only allows explainers to obtain the explainees' background, but also allows explainees to better understand the explanations. In this paper, we propose an approach for an explainer to communicate personalized explanations to an explainee through having consecutive conversations with the explainee. We prove that the conversation terminates due to the explainee's justification of the initial claim as long as there exists an explanation for the initial claim that the explainee understands and the explainer is aware of.