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Starts 7 June 2025 12:56
Ends 7 June 2025
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Overview
Discover how risk-based governance and regulation work in AI through a simplified, jargon-free approach designed for policymakers and industry professionals.
Syllabus
- Introduction to AI Governance and Regulation
- Understanding Risk in AI
- Principles of Risk-Based Governance
- Regulatory Frameworks for AI
- Designing Risk-Based AI Governance Structures
- Ethical Considerations in AI Governance
- Policymaking for AI Regulation
- Industry Perspectives on AI Governance
- Future Directions in AI Governance and Regulation
- Conclusion and Final Thoughts
- Assessment and Feedback
Overview of AI technologies and applications
Importance of governance and regulation in AI
Key stakeholders in AI governance
Definition and types of risks associated with AI
Identifying and assessing AI risks
Case studies of AI risk incidents
Fundamentals of risk-based governance
Comparing risk-based and compliance-based approaches
Key principles for effective risk management
Existing international regulatory frameworks
Emerging trends in AI regulation
The role of standards and best practices
Steps for implementing risk-based governance
Tools and methodologies for risk assessment
Case examples of successful governance models
Ensuring fairness and transparency
Addressing bias and discrimination
Balancing innovation with ethical considerations
Role of policymakers in AI regulation
Strategies for effective policy development
Engaging stakeholders in the regulatory process
Challenges faced by industry professionals
Collaboration between industry and regulators
Best practices from industry case studies
Trends shaping the future of AI governance
Anticipating future risks and regulatory needs
Recommendations for policymakers and industry leaders
Recap of key principles and concepts
Resources for continued learning
Final Q&A and discussion
Course project or case study analysis
Peer review and feedback sessions
Course reflection and future application of skills
Subjects
Business