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In this work, we define concepts within the machine learning context, highlighting their core properties: expressiveness and model-aware inductive bias, and we make explicit the underlying assumption of CBMs. We establish theoretical results for concept-bottleneck models (CBMs), revealing how these properties guide the design of concept sets that optimize model performance.