Whether you're building an internal audit capability or an independent practice, here's how to structure AI auditing work.

Audit Framework

A comprehensive audit framework:

1. Scoping — define the AI system, its context, stakeholders, and audit objectives
2. Risk Assessment — identify potential harms and prioritize focus areas
3. Evidence Gathering — collect technical artifacts, documentation, and data
4. Testing — run fairness, accuracy, explainability, and safety tests
5. Analysis — interpret results in context, considering stakeholder perspectives
6. Reporting — document findings, evidence, and recommendations
7. Remediation Tracking — follow up on implementation of recommendations

Building Internal AI Governance

For organizations building AI:

• AI Ethics Board — cross-functional review body for high-risk AI systems
• Risk Classification — categorize AI systems by potential impact
• Review Gates — mandatory checkpoints in the AI development lifecycle
• Incident Response — process for handling AI-related problems
• Training — ensure all AI practitioners understand responsible AI principles

Third-Party Audit Considerations

For independent auditors:

• Independence — maintain objectivity and avoid conflicts of interest
• Competence — combine technical ML expertise with domain knowledge
• Standards — follow established frameworks (NIST AI RMF, ISO/IEC 42001)
• Methodology — apply consistent, reproducible audit procedures
• Reporting — communicate findings clearly to both technical and non-technical audiences

Career Path in AI Auditing

Becoming an AI auditor:

• Technical Foundation — ML/AI knowledge, statistics, and programming (Python)
• Domain Expertise — deep understanding of the application domain being audited
• Ethics & Policy — knowledge of AI ethics principles and regulatory requirements
• Communication — ability to explain technical findings to diverse audiences
• Certifications — CAIS (Certified AI Security), ISO 42001 auditor certifications

Resources & Community

• NIST AI Risk Management Framework — comprehensive AI risk management guidance
• ISO/IEC 42001 — international standard for AI management systems
• Algorithmic Justice League — advocacy for equitable AI
• Partnership on AI — multi-stakeholder organization for responsible AI
• FAccT Conference — leading academic conference on fairness, accountability, and transparency