AI system verification and validation

Description

Rigorous testing of AI systems for accuracy, fairness, robustness, security, and compliance before and after deployment.

Implementation Guidance

Testing Framework

Define a comprehensive test framework covering: functional accuracy (precision, recall, F1 against holdout data), fairness testing (disparate impact across protected groups), robustness testing (adversarial inputs, distribution shift), security testing (prompt injection, data poisoning, model extraction), and compliance validation (regulatory requirements met).

Independent Validation

Ensure validation is performed by personnel independent from development (per A.3.2). Use separate validation datasets not seen during development. For high-risk systems, engage external validators.

Acceptance Criteria

Define quantitative acceptance thresholds before testing begins: minimum accuracy, maximum bias differential, robustness under defined perturbation levels, and latency requirements. Document pass/fail results and required remediations.

Evidence Requirements

• Test plans with acceptance criteria
• Test execution results and pass/fail records
• Fairness and bias assessment reports
• Security and robustness test results
• Independent validation reports