  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, they are best understood as highly
idealized models. This result has immediate implications for explainable AI
(XAI) and directs philosophical attention toward examining the idealized nature
of DLM representations and their role in future scientific investigation.
