verifiable
This glossary entry explains verifiable for AI governance and model risk programs. The sections below summarize what the term means in plain language, why chief AI officers and cross-functional committees track it, where teams often get confused, and—when you are signed in—how it shows up across major industries and in expectations tied to the EU AI Act and NIST AI RMF. Use related links at the end of the page to explore neighboring concepts without losing context.
What It Means
Verifiable means you can prove your AI system works as intended through concrete evidence and testing that others can independently check. It's about having clear documentation, test results, and audit trails that demonstrate your AI performs correctly and meets its requirements. Think of it as being able to show your work and have someone else confirm you got the right answer.
Why Chief AI Officers Care
Verifiability is essential for regulatory compliance, especially in highly regulated industries where you must prove AI systems work safely and accurately. It also reduces liability risk by providing documented evidence that due diligence was performed, and enables faster issue resolution when problems arise because you have clear records of what was tested and how. Without verifiability, you're essentially asking stakeholders to trust your AI systems on faith alone.
Real-World Example
A healthcare AI system that diagnoses medical conditions must have verifiable performance metrics showing 95% accuracy on standardized test datasets, with detailed logs of which cases it got right or wrong, and documentation that independent medical experts can review to confirm the AI's reasoning process matches accepted medical practices.
Common Confusion
People often confuse verifiable with simply 'working' - but a system can function perfectly yet still not be verifiable if you can't document or prove how it works. Verifiability requires evidence and documentation, not just good performance.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, verifiable means demonstrating through rigorous clinical validation, statistical evidence, and reprodu...
Finance: In finance, verifiable AI means maintaining comprehensive audit trails and documentation that demonstrate model performa...
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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
"can be checked for correctness by a person or tool"Source: ISO/IEC_TS_5723:2022(en)
"provides evidence that the system or system element performs its intended functions and meets all performance requirements listed in the system performance specification and functional and allocated baselines; answers the question, "Did you build the system correctly?" "Source: DOD_TEVV
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