COMPEL Glossary / model-validation
Model Validation
Model validation is the independent assessment of an AI model's performance, fairness, robustness, and compliance before it is deployed to production.
What this means in practice
Validation goes beyond the model developer's own testing to provide an objective evaluation against defined quality thresholds and governance requirements. Validation typically covers technical performance (accuracy, precision, recall across relevant scenarios), fairness (disparate impact analysis across protected groups), robustness (performance under adverse conditions and edge cases), documentation completeness (model cards, data sheets, audit trails), and governance compliance (policy adherence, risk classification, approval documentation). In the COMPEL Stage Gate framework, model validation is a prerequisite for passing Gate E (Validated and Approved) before production deployment can proceed.
Why it matters
Independent validation before production deployment provides objective assurance that AI models meet quality, fairness, and compliance requirements. Relying solely on the development team's own testing creates blind spots because teams naturally focus on scenarios where their models perform well. Independent validators bring fresh perspectives and standardized evaluation criteria that catch issues the development team may have overlooked or rationalized.
How COMPEL uses it
In the COMPEL Stage Gate framework, model validation is a prerequisite for passing Gate E (Validated and Approved) before production deployment. During Model, validation criteria and processes are designed. The Produce stage conducts validation covering technical performance, fairness, robustness, documentation, and governance compliance. The Evaluate stage reviews validation effectiveness by tracking post-deployment issues that validation should have caught.
Related Terms
Other glossary terms mentioned in this entry's definition and context.