This paper aims to clarify the representational status of Deep Learning Models (DLMs). While commonly referred to as 'representations', what this entails is ambiguous due to a conflation of functional and relational conceptions of representation. This paper argues that while DLMs represent their targets in a relational sense, in general, we have no good reason to believe that DLMs encode locally semantically decomposable representations of their targets. That is, the representational capacity these models have is largely global, rather than decomposable into stable, local subrepresentations. This result has immediate implications for explainable AI (XAI) and directs attention toward exploring the global relational nature of deep learning representations and their relationship both to models more generally to understand their potential role in future scientific inquiry.

翻译：本文旨在澄清深度学习模型（DLMs）的表征状态。尽管通常被称为“表征”，但由于功能性与关系性表征概念的混淆，其具体含义存在模糊性。本文认为，虽然DLMs在关系意义上表征其目标对象，但一般而言，我们没有充分理由认为DLMs编码了具有局部语义可分解性的目标表征。换言之，这些模型所具备的表征能力主要是全局性的，而非可分解为稳定、局部的子表征。这一结论对可解释人工智能（XAI）具有直接意义，并将研究导向探索深度学习表征的全局关系本质，以及其与更广义模型的关联，以理解其在未来科学探究中的潜在作用。