Understanding decisions made by neural networks is key for the deployment of intelligent systems in real world applications. However, the opaque decision making process of these systems is a disadvantage where interpretability is essential. Many feature-based explanation techniques have been introduced over the last few years in the field of machine learning to better understand decisions made by neural networks and have become an important component to verify their reasoning capabilities. However, existing methods do not allow statements to be made about the uncertainty regarding a feature's relevance for the prediction. In this paper, we introduce Monte Carlo Relevance Propagation (MCRP) for feature relevance uncertainty estimation. A simple but powerful method based on Monte Carlo estimation of the feature relevance distribution to compute feature relevance uncertainty scores that allow a deeper understanding of a neural network's perception and reasoning.

翻译：理解神经网络的决策机制对于在现实应用中部署智能系统至关重要。然而，这些系统不透明的决策过程在可解释性要求较高的场景中成为了显著缺陷。近年来，机器学习领域引入了大量基于特征的解释技术，旨在提升对神经网络决策过程的理解，这些技术已成为验证其推理能力的重要组成部分。但现有方法无法对预测过程中特征相关性的不确定性进行量化评估。本文提出蒙特卡洛相关性传播方法（MCRP）用于估计特征相关性不确定性。该方法通过蒙特卡洛估计特征相关性分布，以计算特征相关性不确定性分数，从而实现对神经网络感知与推理过程的深度解析。这是一种简洁而强大的方法论框架。