AI practitioners, risk managers, compliance officers, data scientists, IT leaders, and professionals responsible for AI governance and responsible AI adoption

Basic understanding of AI/ML concepts and organizational risk management practices

Course Outline:

Module 1: Introduction to AI Risk and Governance

1. What is AI risk
2. Importance of governance in AI systems
3. Overview of global AI regulations and standards

Module 2: Overview of NIST AI Risk Management Framework (AI RMF)

1. Core functions: Govern, Map, Measure, Manage
2. Framework structure and key concepts
3. Trustworthy AI characteristics

Module 3: Identifying and Mapping AI Risks

1. Types of AI risks (bias, security, privacy, operational)
2. Risk identification techniques
3. Contextualizing AI use cases

Module 4: Measuring and Evaluating Risks

1. Risk assessment methodologies
2. Metrics for fairness, accuracy, and robustness
3. Testing and validation approaches

Module 5: Managing and Mitigating Risks

1. Risk mitigation strategies
2. Governance controls and policies
3. Monitoring and continuous improvement

Module 6: Implementing AI Governance in Organizations

1. Roles and responsibilities
2. Integrating AI RMF into business processes
3. Documentation and audit readiness

Module 7: Ethics, Compliance, and Future Trends

1. Ethical considerations in AI
2. Regulatory landscape and compliance
3. Emerging trends in AI governance