Compliance
AI Testing and Validation
Systematic evaluation of AI systems for performance, safety, fairness, and compliance.
Definition
AI Testing and Validation encompasses systematic evaluation of AI systems before and during deployment. Key testing types include:
- Functional testing: Verifying AI performs intended functions correctly
- Performance testing: Measuring accuracy, latency, throughput under various conditions
- Fairness testing: Assessing for bias and discrimination across demographic groups
- Robustness testing: Evaluating behavior under adversarial inputs and edge cases
- Security testing: Identifying vulnerabilities including prompt injection, data poisoning
- Regression testing: Ensuring updates don't degrade performance or introduce issues
Regulatory frameworks increasingly require documented testing. SR 11-7 mandates model validation in banking; EU AI Act requires testing for high-risk systems; Colorado AI Act requires impact assessments.
Sources
- •EU AI Act Article 9
- •NIST AI RMF
- •ISO/IEC 25000
Related Terms
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Ongoing surveillance of AI systems in production to detect drift, failures, and compliance issues....
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post-deployment testing
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Testing in Real-World Conditions
Temporary AI testing outside laboratory conditions....
testing under real-world conditions
Controlled trials of AI systems outside laboratory settings....