AI Audit Framework

Systematic AI auditing provides assurance that AI systems behave as intended, fairly, and safely.

## Types of AI Audits

### Internal Audits - Conducted by internal teams before and during deployment - Focus on technical performance and policy compliance - Ongoing monitoring and periodic reviews - Essential for high-risk AI systems

### External Audits - Independent third-party assessment - Required by regulation for highest-risk systems - Provides objective assurance to stakeholders - May be required for regulatory compliance

### Algorithmic Audits - Focus on decision-making processes and outcomes - Assess bias and fairness across demographic groups - Evaluate model transparency and explainability - Assess sociotechnical impacts

## Audit Scope

### TEVV Framework (NIST AI RMF) - Test: Measure system performance against requirements - Evaluate: Assess AI system characteristics and context - Verify: Confirm requirements are met as designed - Validate: Confirm AI system works for intended purpose

### Audit Dimensions ``` 1. Performance Testing - Accuracy, precision, recall, F1 - Subgroup performance analysis - Edge case and stress testing

1. Fairness Assessment
2. - Demographic parity
3. - Equal opportunity
4. - Disparate impact analysis
5. - Intersectional fairness

1. Robustness Testing
2. - Adversarial attacks
3. - Out-of-distribution inputs
4. - Data corruption scenarios

1. Transparency Audit
2. - Explainability of decisions
3. - Documentation completeness
4. - Human review mechanisms

1. Governance Review
2. - Policy compliance
3. - Accountability structures
4. - Incident history
5. ```