Whitepaper
Human-Verifiable Decision Systems
Making AI recommendations inspectable, challengeable, traceable, and useful.
Abstract
Trust in applied AI does not come from aesthetics or confidence alone. It comes from whether a human can verify why the system produced a recommendation, what evidence it used, what uncertainty remains, and what should happen if the recommendation is challenged.
Output Contract
A human-verifiable system standardizes the minimum content of consequential output: recommendation, severity, confidence, reasons, provenance, assumptions, missing information, invalidation path, next action, approval requirement, and trace reference.
Core ideaVerification is not only a trust feature. It is a learning feature.