The working A prototype: use its controls to test the build question.
Decide whether an AI-assisted system is ready for consequential real-world use.
RN conceived the question, structured the evidence boundaries, designed the experience, implemented the prototype and documented its limits.
This independent prototype demonstrates an approach; it is not a deployed client system, professional advice or proof of real-world outcomes.
Work through the controls with a situation of your own rather than the sample values. The tool responds to what you put in, so the useful output comes from real input, and everything is processed in your browser as you go. Any sample content you find already loaded is there to show the shape of a filled-in state, and you can clear it and start again at any point.
The build overview sets out the question, the purpose, the role and the evidence status in one place, and the public record behind it states what changed, what another person can reuse and what supports the system. If you want to judge how far this build should be trusted, the record is the page to read, not this one.
BUILD 032-A
Consequential AI Release Readiness Harness
Decide whether an AI-assisted system is ready for consequential real-world use.
BLOCKED
3 / 8 evidence outcomes pass · residual risk unknown · 17 blockers
This synthetic harness inventories readiness evidence for a declared consequential-use scope. It cannot approve or authorize release, validate evidence, certify safety, or establish compliance.
Required dependency gates
Non-claims and expiry
- Completeness is not approval, deployment authorization, safety certification, legal compliance, or evidence validation.
- Synthetic evidence demonstrates schema behavior only; production tests, affected-person input, security review, and authorized release governance remain external.
- A passing historical test can expire, be revoked, become inapplicable after change, or fail to represent real operating conditions.
Every evidence record is scoped, versioned, dated, expiring, and revocable. Any system, model, workflow, population, environment, or risk change requires applicability review and usually new evidence.