Validation
The process of confirming that an AI model performs accurately and reliably on intended tasks and meets defined performance criteria.
A comprehensive set of checks - including evaluation on hold-out test sets, stress tests under edge-case scenarios, fairness audits across subgroups, and security assessments - that verify a model’s readiness for production. Validation involves independent review by a validation team, documentation of methods and results in a formal validation report, and explicit sign-off before deployment.
A medical‐imaging AI undergoes validation by running on a curated test suite of rare tumor cases, assessing sensitivity and specificity, performing fairness checks across age groups, and simulating noisy inputs. Only after passing all criteria and obtaining sign-off does it receive authorization for clinical use.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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