Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. Participants are presented with static explanations, followed by a conversation with a human expert regarding the explanations. We measure the effect of the conversation on participants' ability to choose, from three machine learning models, the most accurate one based on explanations and their self-reported comprehension, acceptance, and trust. Empirical results show that conversations significantly improve comprehension, acceptance, trust, and collaboration. Our findings highlight the importance of customized model explanations in the format of free-form conversations and provide insights for the future design of conversational explanations.

翻译：可解释人工智能（XAI）研究旨在揭示不透明人工智能模型的决策过程。迄今为止，大多数XAI方法提供的是一次性、静态的解释，无法适应不同用户的背景和理解水平。本文探究自由形式的对话是否能增强用户对静态解释的理解、提升对解释方法的接受度和信任度，并促进人机协作。参与者先获得静态解释，随后与人类专家就该解释进行对话。我们测量对话对参与者基于解释从三个机器学习模型中选择最准确模型的能力的影响，以及他们自我报告的理解、接受度和信任度。实证结果表明，对话显著提升了理解、接受、信任和协作。我们的发现强调了以自由形式对话为基础定制化模型解释的重要性，并为未来对话式解释的设计提供了见解。