Standards for AI twin validation

An industry standard requiring auditable, repeatable validation of one‑to‑one AI 'digital twins' against verified humans, including simultaneous validation protocols, confidence calibration, provenance of training signals, and public reporting of sample/holdout tests. The standard would mandate metrics (e.g., person‑level accuracy, confidence‑calibration) and procedural safeguards (ID‑verification, refresh cadence) so clients can judge reliability. — If adopted, it would set the threshold for when simulated human data can be used commercially or politically, affecting polling integrity, consumer protections, and regulation.