The article emphasizes that AI Governance should not be the sole responsibility of AI/ML teams, but should involve organizational and use case processes and documentation. It also highlights the importance of understanding the different levels of AI Governance abstraction for different stakeholders, including regulators and the general public. The article suggests that upcoming regulations will likely focus on organizational and use case governance levels, as defining model level requirements can be challenging and may hamper innovation.
Key takeaways:
- AI Governance is a holistic term that can be broken down into three levels: Organizational, Use Case, and Model. Each level has different responsibilities and requirements for effective governance.
- Organizational Level Governance involves creating clear internal policies for AI ethics, accountability, safety, etc., and ensuring these policies are enforced. Senior leaders and legal teams are primarily responsible for this level.
- Use Case Level Governance focuses on ensuring a specific application of AI meets all necessary governance standards. This level requires careful documentation of the goals, justifications, and risks of using AI for a specific task.
- Model Level Governance is focused on ensuring the technical function of an AI system meets expected standards of fairness, accuracy, and security. AI/ML teams are primarily responsible for this level.