论文标题
因果关系 - 局部可解释的模型 - 不合SNOSTIC解释
Causality-Aware Local Interpretable Model-Agnostic Explanations
论文作者
论文摘要
可解释的人工智能（XAI）方法的主要缺点是特征独立性假设，阻碍了对潜在可变依赖性的研究。通过分析对原始样本中可能很少发生的随机生成特征值的影响，这会导致近似黑匣子行为。本文通过将因果知识整合到XAI方法中来提高透明度并使用户能够评估生成的解释的质量来解决这个问题。具体而言，我们向广泛使用的局部和模型不合时宜的解释器提出了一种新颖的扩展，该解释器编码了所解释的实例的数据中的明确因果关系。广泛的实验表明，我们的方法在忠实地复制黑框模型的机制以及生成的解释的一致性和可靠性方面克服了原始方法。
A main drawback of eXplainable Artificial Intelligence (XAI) approaches is the feature independence assumption, hindering the study of potential variable dependencies. This leads to approximating black box behaviors by analyzing the effects on randomly generated feature values that may rarely occur in the original samples. This paper addresses this issue by integrating causal knowledge in an XAI method to enhance transparency and enable users to assess the quality of the generated explanations. Specifically, we propose a novel extension to a widely used local and model-agnostic explainer, which encodes explicit causal relationships within the data surrounding the instance being explained. Extensive experiments show that our approach overcomes the original method in terms of faithfully replicating the black-box model's mechanism and the consistency and reliability of the generated explanations.
