Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models. These models, however, are generally regarded as black boxes, which, in spite of their performance, could prevent their use. In this context, the field of eXplainable AI attempts to develop techniques that temper the impenetrable nature of the models and promote a level of understanding of their behavior. Here we present our contribution to XAI methods in the form of a framework that we term SpecXAI, which is based on the spectral characterization of the entire network. We show how this framework can be used to not only understand the network but also manipulate it into a linear interpretable symbolic representation.

翻译：深度学习凭借其将大量数据转化为高性能模型的能力，正日益被商业和工业领域所采用。然而，这些模型通常被视为黑箱，这虽不影响其性能，却可能阻碍它们的应用。在此背景下，可解释人工智能领域致力于开发能够缓和模型的不可穿透性、并提升对其行为理解水平的技术。本文我们提出了对XAI方法的贡献，以一种我们称为SpecXAI的框架形式呈现，该框架基于整个网络的光谱表征。我们展示了该框架如何不仅可用于理解网络，还可将其操控为一种线性可解释的符号表征。