Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging. Existing approaches often focus on explaining how changing individual inputs affects NNs' outputs. However, an explanation that is consistent with the input-output behaviour of an NN is not necessarily faithful to the actual mechanics thereof. In this paper, we exploit relationships between multi-layer perceptrons (MLPs) and quantitative argumentation frameworks (QAFs) to create argumentative explanations for the mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining as much of the original structure as possible. It then translates the sparse MLP into an equivalent QAF to shed light on the underlying decision process of the MLP, producing global and/or local explanations. We demonstrate experimentally that SpArX can give more faithful explanations than existing approaches, while simultaneously providing deeper insights into the actual reasoning process of MLPs.

翻译：神经网络在人工智能中具有多种应用，但解释其决策仍然具有挑战性。现有方法通常侧重于解释改变单个输入如何影响神经网络的输出。然而，与神经网络输入-输出行为一致的解释并不一定忠实于其实际机制。在本文中，我们利用多层感知器与定量论证框架之间的关系，为多层感知器的机制创建论证性解释。我们的SpArX方法首先在尽可能保留原始结构的前提下对多层感知器进行稀疏化，然后将稀疏多层感知器转换为等价的定量论证框架，以揭示多层感知器的底层决策过程，生成全局和/或局部解释。我们通过实验证明，SpArX能提供比现有方法更忠实的解释，同时更深入地洞察多层感知器的实际推理过程。