Certified AI Governance Professional (AIGP)

There are no prerequisites for this course.

Who Should Train?

We must continue to build and refine the governance processes through which trustworthy AI will emerge and we must invest in the people who will build ethical and responsible AI. Those who work in compliance, privacy, security, risk management, legal, HR and governance together with data scientists, AI project managers, business analysts, AI product owners, model ops teams and others must be prepared to tackle the expanded equities at issue in AI governance.

Including any professionals tasked with developing AI governance and risk management in their operations, and anyone pursuing IAPP Artificial Intelligence Governance Professional (AIGP) certification.

AIGP training teaches how to develop, integrate, and deploy trustworthy AI systems in line with emerging laws and policies. The curriculum provides an overview of AI technology, survey of current law, and strategies for risk management, security and safety considerations, privacy protection and other topics.

• Establish foundational knowledge of AI systems and their use cases, the impacts of AI, and comprehension of responsible AI principles.
• Demonstrate an understanding of how current and emerging laws apply to AI systems, and how major frameworks are capable of being responsibly governed.
• Show comprehension of the AI life cycle, the context in which AI risks are managed, and the implementation of responsible AI governance.
• Presents awareness of unforeseen concerns with AI and knowledge of debated issues surrounding AI governance.

This training teaches critical AI governance concepts that are also integral to the AIGP certification exam. While not purely a “test prep” course, this training is appropriate for professionals who plan to certify, as well as for those who want to deepen their AI governance knowledge. Both the training and the exam are based on the same body of knowledge.

Module 1: Foundations of artificial intelligence

Defines AI and machine learning, presents an overview of the different types of AI systems and their use cases, and positions AI models in the broader socio-cultural context.

• Understand the differences among types of AI systems.
• Understand the AI technology stack.
• Understand AI and the evolution of data science.

Module 2: AI impacts on people and responsible AI principles

Outlines the core risks and harms posed by AI systems, the characteristics of trustworthy AI systems, and the principles essential to responsible and ethical AI.

• Understand the core risks and harms posed by AI systems.
• Understand the characteristics of trustworthy AI systems.

Module 3: AI development life cycle

Describes the AI development life cycle and the broad context in which AI risks are managed.

• Understand the similarities and differences among existing and emerging ethical guidance on AI.
• Understand the existing laws that interact with AI use.
• Understanding key GDPR intersections.
• Understanding liability reform.

Module 4: Implementing responsible AI governance and risk management

Explains how major AI stakeholders collaborate in a layered approach to manage AI risks while acknowledging AI systems’ potential societal benefits.

• Understanding the requirements of the EU AI Act.
• Understand other emerging global laws.
• Understand the similarities and differences among the major risk management frameworks and standards.

Module 5: Implementing AI projects and systems

Outlines mapping, planning and scoping AI projects, testing and validating AI systems during development, and managing and monitoring AI systems after deployment.

• Understand the key steps in the AI system planning phase.
• Understand the key steps in the AI system design phase.
• Understand the key steps in the AI system development phase.
• Understand the key steps in the AI system implementation phase.

Module 6: Current laws that apply to AI systems

Surveys the existing laws that govern the use of AI, outlines key GDPR intersections, and provides awareness of liability reform.

• Ensure interoperability of AI risk management with other operational risk strategies
• Integrate AI governance principles into the company.
• Establish an AI governance infrastructure.
• Map, plan and scope the AI project.
• Test and validate the AI system during development.
• Manage and monitor AI systems after deployment.

Module 7: Existing and emerging AI laws and standards

Describes global AI-specific laws and the major frameworks and standards that exemplify how AI systems can be responsibly governed.

• Awareness of legal issues.
• Awareness of user concerns.
• Awareness of AI auditing and accountability issues.

Module 8: Ongoing AI issues and concerns

Presents current discussions and ideas about AI governance, including awareness of legal issues, user concerns, and AI auditing and accountability issues.