This paper introduces the front-propagation algorithm, a novel eXplainable AI
(XAI) technique designed to elucidate the decision-making logic of deep neural
networks. Unlike other popular explainability algorithms such as Integrated
Gradients or Shapley Values, the proposed algorithm is able to extract an
accurate and consistent linear function explanation of the network in a single
forward pass of the trained model. This nuance sets apart the time complexity
of the front-propagation as it could be running real-time and in parallel with
deployed models. We packaged this algorithm in a software called
$\texttt{front-prop}$ and we demonstrate its efficacy in providing accurate
linear functions with three different neural network architectures trained on
publicly available benchmark datasets.

该论文介绍了一种前向传播算法，一种新颖的可解释人工智能（XAI）技术，旨在阐明深度神经网络的决策逻辑。与其他流行的可解释性算法（如集成梯度或 Shapley 值）不同，该算法能够在训练模型的单次前向传播中提取网络的准确且一致的线性函数解释。我们将该算法打包在一个名为 front-prop 的软件中，并演示了它在使用公开可用的基准数据集训练的三种不同神经网络架构中提供准确的线性函数的功效。