AI Project Governance Practitioner Training in Copenhagen, Denmark

This training equips professionals with the principles and practices to effectively govern AI projects. Participants will learn to establish governance structures, manage risks, ensure ethical compliance, and align AI initiatives with organizational strategy. The course covers the complete AI project lifecycle, from inception to deployment and monitoring, with a focus on accountability, transparency, and value realization.

Foundations of AI Project Governance

• Defining AI project governance and its importance
• Key principles: accountability, transparency, fairness
• Governance vs. management in AI projects
• Overview of AI governance frameworks (e.g., NIST, ISO)

Hands-on exercises

AI Project Lifecycle and Governance Checkpoints

• Phases of an AI project: ideation, development, deployment, monitoring
• Governance checkpoints at each phase
• Gate reviews and decision criteria
• Documentation and audit trails

Hands-on exercises

Risk Management for AI Projects

• Identifying AI-specific risks (data, model, operational)
• Risk assessment methodologies (qualitative and quantitative)
• Mitigation strategies and contingency planning
• Monitoring and reporting risks

Hands-on exercises

Ethical AI and Bias Mitigation

• Ethical principles in AI: fairness, accountability, transparency
• Sources of bias in data and algorithms
• Bias detection and mitigation techniques
• Ethical review boards and processes

Hands-on exercises

Regulatory Compliance and Legal Considerations

• Key regulations: GDPR, EU AI Act, CCPA
• Compliance requirements for AI systems
• Data privacy and security considerations
• Intellectual property and liability issues

Hands-on exercises

Stakeholder Engagement and Communication

• Identifying and mapping AI project stakeholders
• Communication strategies for diverse audiences
• Reporting governance metrics and KPIs
• Building trust and managing expectations

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AI Project Governance Tools and Technologies

• Governance platforms and software solutions
• Model monitoring and observability tools
• Data lineage and provenance tracking
• Automation of governance processes

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Case Studies and Best Practices in AI Governance

• Industry case studies: successes and failures
• Lessons learned from real-world AI projects
• Developing a governance playbook for your organization
• Future trends in AI governance

Hands-on exercises