As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the outputs of) AI models are growing, but the state-of-the-art predominantly focuses on static explanations. In this paper, we focus instead on interactive explanations framed as conflict resolution between agents (i.e. AI models and/or humans) by leveraging on computational argumentation. Specifically, we define Argumentative eXchanges (AXs) for dynamically sharing, in multi-agent systems, information harboured in individual agents' quantitative bipolar argumentation frameworks towards resolving conflicts amongst the agents. We then deploy AXs in the XAI setting in which a machine and a human interact about the machine's predictions. We identify and assess several theoretical properties characterising AXs that are suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent behaviours, e.g. capturing counterfactual patterns of reasoning in machines and highlighting the effects of cognitive biases in humans. We show experimentally (in a simulated environment) the comparative advantages of these behaviours in terms of conflict resolution, and show that the strongest argument may not always be the most effective.

翻译：随着可解释人工智能（XAI）领域的成熟，对AI模型输出进行交互式解释的需求日益增长，但当前主流研究仍主要聚焦于静态解释。本文则聚焦于交互式解释，通过利用计算论辩方法，将其定义为智能体（即AI模型和/或人类）之间的冲突解决过程。具体而言，我们定义了论辩交换（Argumentative eXchanges, AXs），用于在多智能体系统中动态共享各智能体定量双极论辩框架中的信息，以解决智能体间的冲突。随后，我们将AXs部署到XAI场景中，使机器与人类围绕机器的预测结果进行交互。我们识别并评估了适用于XAI的AXs的若干理论特性。最后，通过定义多种智能体行为（如捕捉机器中的反事实推理模式、突出人类认知偏差的影响），将AXs实例化于XAI场景。实验（在模拟环境中）表明这些行为在冲突解决方面的相对优势，并证明最强论点未必总是最有效的。