From Evidence to Verifiability: Rebuilding Trust in AI Outputs 🔏
⏰ TLDR
This work shows that the hardest part of using AI in high-trust environments is not the model, but the policy. Once editorial policy is made explicit and executable, AI systems become interchangeable the real challenge is engineering reliable measurements and deterministic enforcement of those policies. This reframes AI reliability as a policy and measurement problem, not a model problem.
đź“‹ Summary
AI systems are becoming deeply embedded in how we research, write, and reason. At the same time, their use in high-trust environments is under strain — not because models are incapable, but because they are being deployed into settings that demand determinism, provenance, and enforceable rules.