Validation
The process of confirming that an AI model performs accurately and reliably on intended tasks and meets defined performance criteria.
Definition
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.
Real-World Example
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.
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