validation
What It Means
Validation is proving that your AI system actually solves the real business problem it was designed for, in the actual environment where it will be used. It's like test-driving a car on real roads with real traffic, not just checking that the engine runs in a garage. This involves measuring whether the system meets stakeholder needs and performs effectively under realistic operating conditions.
Why Chief AI Officers Care
Validation directly impacts business ROI and regulatory compliance - a system that works in the lab but fails with real customers or data creates massive financial and reputational risks. For AI systems handling sensitive data or critical decisions, validation evidence is often required for audits, certifications, and regulatory approval. Without proper validation, you risk deploying systems that don't actually improve business outcomes despite significant investment.
Real-World Example
A bank develops an AI fraud detection system that shows 95% accuracy in testing with clean historical data. During validation, they deploy it in a pilot environment with live transactions, messy real-world data, and actual customer behavior patterns. They discover the system flags too many legitimate transactions as fraud, creating customer service issues and lost revenue - requiring significant adjustments before full deployment.
Common Confusion
People often confuse validation with verification or testing - validation specifically asks 'are we building the right thing' while verification asks 'are we building the thing right.' Validation focuses on real-world effectiveness and stakeholder satisfaction, not just technical correctness or meeting specifications.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, validation requires demonstrating that your system improves patient outcomes, workflow efficiency, or ...
Finance: In finance, validation ensures AI models like credit scoring algorithms or fraud detection systems actually reduce defau...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Confirmation by examination and provision of objective evidence that the particular requirements for a specific intended use are fulfilled."Source: UNODC_Glossary_QA_GLP
"Confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled."Source: IEEE_Soft_Vocab
"provides objective evidence that the capability provided by the system complies with stakeholder performance requirements, achieving its use in its intended operational environment; answers the question, "Is it the right solution to the problem?" [C]onsists of evaluating the operational effectiveness, operational suitability, sustainability, and survivability of the system or system elements under operationally realistic conditions."Source: DOD_TEVV
"A continuous monitoring of the process of compilation and of the results of this process."Source: OECD
Related Terms
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