定量双极论证框架中的对比式解释

中文摘要 AI 辅助

论证框架是在多种场景下表示和推理信息的有用工具，例如在AI模型执行分类任务时为其提供补充，其显著优势是能提供额外的可解释性。本文针对定量双极论证框架（QBAFs）这一形式体系，引入了对比式解释。与现有大多数解释QBAF的方法（仅解释单个目标论证（即主题论证）的推理结果）不同，对比式解释用于解释两个主题论证之间的差异。我们引入了对比归因函数（CAFs）的一般形式，并确定了其应满足的一组通用属性。我们引入了基于移除、梯度和夏普利值的CAFs，并研究了它们的属性。最后，为说明对比式解释的实用性，我们在医疗和偏见识别场景中展示了其作用。

英文摘要

Argumentation frameworks are useful tools for representing and reasoning with information in a variety of settings, e.g. in supplementing AI models as they perform classification tasks, with a notable benefit of providing additional explainability. In this paper, we introduce contrastive explanations for Quantitative Bipolar Argumentation Frameworks (QBAFs), one such formalism. Unlike most existing explanations for QBAFs, which explain the reasoning outcome of a single argument of interest (i.e. a topic argument), contrastive explanations explain the difference between two topic arguments. We introduce a general form of contrastive attribution functions (CAFs) and establish a set of general properties they should satisfy. We introduce CAFs based on removal, gradients and Shapley-values, and study their properties. Finally, to illustrate contrastive explanations, we demonstrate their usefulness in healthcare and bias identification settings.

发表机构

• Imperial College London（帝国理工学院）
• Cardiff University（卡迪夫大学）

机构由 AI 辅助整理，请以论文原文为准。