Model Epistemology and the Question of Machine Knowledge

As AI systems become operational decision participants, technical metrics alone are no longer enough.

We need a clearer account of what models know, what they approximate, and where their confidence should be bounded. This is not an abstract exercise; epistemic ambiguity directly affects system design, risk handling, and human oversight.

This note frames model epistemology as a practical architecture concern, not just a philosophical side topic.