Model Semantics refers to the meaning and interpretation of the representations, outputs, and internal structures of a computational model, particularly in machine learning. It describes how model inputs are mapped to outputs and how learned features correspond to real-world concepts or variables. Understanding model semantics is important for interpretability, validation, and trust, enabling users to assess whether a model captures meaningful relationships and behaves consistently across different scenarios.
AI interpretability has long been the focus, but what if it’s only part of the story? New research introduces model semantics, a framework for understanding what AI systems truly represent and how their internal structures connect to real-world phenomena.
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