AI Governance, Risk and Responsible Deployment Training in Thimphu, Bhutan

This training program equips professionals with the knowledge and skills to establish robust AI governance structures, identify and mitigate AI-related risks, and ensure responsible deployment of AI technologies. Participants will explore regulatory landscapes, ethical considerations, and practical frameworks for managing AI systems throughout their lifecycle. The course emphasizes real-world applications and hands-on exercises to build actionable expertise.

Foundations of AI Governance

• Definition and importance of AI governance
• Key principles: accountability, transparency, fairness, privacy
• Overview of global regulatory landscape
• Governance models and organizational structures

Hands-on exercises

Risk Management Frameworks for AI

• Risk identification: algorithmic, operational, reputational
• Risk assessment methodologies (qualitative and quantitative)
• Risk mitigation strategies and controls
• Continuous monitoring and reporting

Hands-on exercises

Ethical AI and Fairness

• Ethical principles in AI development
• Bias detection and mitigation techniques
• Fairness metrics and trade-offs
• Inclusive design and stakeholder engagement

Hands-on exercises

Transparency and Explainability

• Explainability methods (LIME, SHAP, etc.)
• Documentation standards for AI systems
• Communicating AI decisions to non-technical audiences
• Model cards and data sheets

Hands-on exercises

Regulatory Compliance and Legal Aspects

• GDPR, EU AI Act, and other key regulations
• Compliance auditing and reporting
• Data protection impact assessments
• Cross-border data transfer considerations

Hands-on exercises

Accountability and Human Oversight

• Designating responsible parties and roles
• Human-in-the-loop and human-on-the-loop models
• Escalation and incident response procedures
• Governance committees and review boards

Hands-on exercises

AI System Lifecycle Governance

• Governance from design to decommissioning
• Model validation and testing protocols
• Version control and change management
• Post-deployment monitoring and maintenance

Hands-on exercises

Audit, Monitoring, and Continuous Improvement

• Internal and external audit processes
• Key performance indicators for governance
• Feedback loops and continuous improvement
• Tools and technologies for AI governance

Hands-on exercises

Stakeholder Communication and Change Management

• Reporting to board and executive leadership
• Engaging with regulators and the public
• Training and awareness programs
• Building a culture of responsible AI

Hands-on exercises

Case Studies and Emerging Trends

• Analysis of real-world AI governance failures
• Lessons learned from industry best practices
• Future directions: AI governance and emerging technologies
• Action planning for organizational implementation

Hands-on exercises

Summary and Conclusion