Certified AI Governance Professional (AIGP)

With the rapid development of artificial intelligence technologies, the need for responsible AI governance continues to grow. The AIGP (Artificial Intelligence Governance Professional) certification confirms competencies in ensuring safety, ethics, and trustworthiness throughout the creation, implementation, and management of AI systems. The course prepares participants to understand fundamental principles, legal regulations, and practical aspects of AI risk management and accountability.

Why obtain the AIGP certification?

• Gain foundational knowledge of AI systems, their applications, and the principles of responsible AI governance.
• Understand how current and upcoming legal regulations impact AI systems and how to apply ethical frameworks.
• Learn about the AI lifecycle and how to manage AI-related risks.
• Develop awareness of potential legal issues, ethical disputes, and user concerns.
• Validate your knowledge and skills according to the international AIGP standard.

Delegates will learn to:

The AIGP course covers key topics related to responsible AI governance, including:

• develop, integrate, and implement trustworthy AI systems compliant with regulations,
• apply strategies for risk management, security, and privacy protection,
• understand the AI lifecycle and methods of its ethical oversight,
• prepare for the AIGP certification exam, although the course has a broader, practical focus.

Who should attend?

This course is designed for professionals working in compliance, privacy, security, risk management, legal departments, HR, and governance. Data scientists, AI project managers, business analysts, product owners, Model Ops teams, and individuals responsible for AI implementation will also find valuable, practical insights.

The course is especially recommended for those planning to obtain the Artificial Intelligence Governance Professional (AIGP) certification.

Prerequisites

There are no prerequisites for this course.

Module 1: Foundations of Artificial Intelligence

• Definition of artificial intelligence and machine learning.
• Types of AI systems and their applications.
• Positioning AI models in a socio-cultural context.
• Differences between types of AI systems and the AI stack.
• The evolution of AI and data science.

Module 2: AI’s Impact on People and Principles of Responsible AI

• Risks and harms caused by AI.
• Characteristics of trustworthy AI systems.
• Principles of responsible and ethical AI.

Module 3: The AI Development Lifecycle

• Overview of the AI lifecycle.
• Risks and context of AI governance.
• AI-related law and key intersections with GDPR.
• Liability reform.

Module 4: Implementing Responsible AI Risk Management

• Stakeholder roles in AI governance.
• Requirements of the EU AI Act.
• Global AI regulations.
• Risk management frameworks and international standards.

Module 5: Implementing AI Projects and Systems

• AI project planning and mapping.
• Testing and validating AI systems.
• Post-deployment management and monitoring.

Module 6: Current Legal Frameworks for AI Systems

• Legal regulations concerning AI.
• Integrating AI governance with other risk strategies.
• Building AI governance infrastructure.

Module 7: Existing and Emerging AI Laws and Standards

• Global AI-specific legislation.
• AI ethical frameworks and standards.
• Awareness of legal, audit, and liability issues related to AI.

Module 8: Current AI-Related Issues and Concerns

• Ongoing discussions on AI: legal challenges, user concerns, audit, and accountability.