Explainability has become a valuable tool in the last few years, helping humans better understand AI-guided decisions. However, the classic explainability tools are sometimes quite limited when considering high-dimensional inputs and neural network classifiers. We present a new explainability method using theoretically proven high-dimensional properties in neural network classifiers. We present two usages of it: 1) On the classical sentiment analysis task for the IMDB reviews dataset, and 2) our Malware-Detection task for our PowerShell scripts dataset.

翻译：可解释性在过去几年中已成为一种有价值的工具，帮助人类更好地理解AI辅助决策。然而，经典的可解释性工具在处理高维输入和神经网络分类器时有时相当有限。我们提出了一种利用神经网络分类器中经过理论验证的高维特性的新可解释性方法。我们展示了它的两种应用：1) 针对IMDB评论数据集的经典情感分析任务，以及2) 针对我们的PowerShell脚本数据集的恶意软件检测任务。