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

2. Fairness Assessment
   - Demographic parity
   - Equal opportunity
   - Disparate impact analysis
   - Intersectional fairness

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

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

5. Governance Review
   - Policy compliance
   - Accountability structures
   - Incident history