论文标题
用论证解释因果模型：双变量强化的情况
Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement
论文作者
论文摘要
因果模型在机器学习中起着越来越重要的作用，尤其是在可解释的AI领域。我们介绍了一种概念化，用于从因果模型中生成论证框架（AFS），以便为模型的输出锻造解释。该概念化是基于AFS语义作为解释模式的重新解释的理想特性，这是为了表征因果模型中的关系的意思。我们通过重新诠释双变量增强的特性来证明我们的方法论，作为解释双极AFS作为因果模型输出的解释。我们对这些论点解释进行了理论评估，研究了它们是否满足了一系列理想的解释和论证属性。
Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for the models' outputs. The conceptualisation is based on reinterpreting desirable properties of semantics of AFs as explanation moulds, which are means for characterising the relations in the causal model argumentatively. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement as an explanation mould to forge bipolar AFs as explanations for the outputs of causal models. We perform a theoretical evaluation of these argumentative explanations, examining whether they satisfy a range of desirable explanatory and argumentative properties.
