TL;DR
Plausible AI output needs challenge protocols that make confidence testable before people rely on it.
What the paper develops
Plausibility is one of the most useful qualities of generative AI and one of its most dangerous. This paper examines what happens when fluent output enters a workflow without a challenge protocol, independent evidence, or a named person able to reject it. It turns “verify the output†into an operating control by defining the questions, records, thresholds, and escalation paths that make confidence testable before reliance expands.
The operating move
Add a challenge protocol before consequential AI output can be relied on: identify assumptions, compare independent evidence, record uncertainty, and name the person who can stop or correct the decision.
WORKFLOWCONTROL EVIDENCEHUMAN OWNER
Inside the white paper
- Why fluent output bypasses ordinary skepticism in consequential work
- A repeatable challenge protocol for assumptions, evidence, and uncertainty
- Ownership, escalation, and learning when an output fails review