Certified AI Governance Professional (AIGP)

With the rapid advancement of artificial intelligence technology, there is a growing need for responsible AI governance. The AIGP (Artificial Intelligence Governance Professional) certificate validates competence in ensuring safety, ethics, and trust throughout the development, deployment, and management of AI systems. This training prepares participants to understand fundamental principles, legal regulations, and practical aspects of managing AI risk and accountability.

Delegates Will Learn:

The AIGP training covers key topics in responsible AI governance, including:

• Developing, integrating, and deploying trustworthy AI systems compliant with regulations,
• Strategies for risk management, security, and privacy protection,
• The AI lifecycle and methods for its ethical oversight,
• Preparation for the AIGP certification exam, although the training has a broader practical focus.

Who Should Attend?

This training is designed for professionals working in compliance, privacy, security, risk management, legal affairs, HR, and governance. It also benefits data scientists, AI project managers, business analysts, product owners, Model Ops teams, and anyone responsible for implementing AI within organizations.

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

Prerequisites

There are no prerequisites for this course.

Module 1: Artificial Intelligence Fundamentals

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

Module 2: Impact of AI 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: AI Development Lifecycle

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

Module 4: Implementing Responsible AI Risk Management

• The role of stakeholders 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

• Planning and mapping AI projects.
• Testing and validating AI systems.
• Post-deployment management and monitoring.

Module 6: Current Laws Applied to 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 specialized law for AI.
• AI ethics frameworks and standards.
• Awareness of legal, audit, and AI accountability issues.

Module 8: Current Issues and Concerns Related to AI

• Current AI discussions: legal matters, user concerns, auditing, and accountability.