What you will learn

Delegates at this training will learn critical artificial intelligence 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

What is included in the course fee?

To complement your training, the course fee includes:

• Enrollment to participate in IAPP accredited AIGP class
• IAPP Accredited training materials and learning resources
• AIGP Exam Voucher for online proctored exam, valid for 12 months
• One Year of IAPP Membership (renewed if existing member)

Prerequisites

There are no formal pre-requisites for AIGP but course delegates would benefit from some prior experience in field of AI and Data Privacy.

Target audience

Any professionals tasked with developing AI governance and risk management in their operations, and anyone pursuing IAPP Artificial Intelligence Governance Professional certification.

Learning Objectives

At the end of this course, you will be able to:

• Understand the technological foundations of artificial intelligence (AI) and the AI development lifecycle.
• Evaluate AI’s effects on people and apply appropriate principles.
• Know how current and emerging laws apply to AI systems.
• Implement responsible AI governance and risk management.
• Contemplate ongoing issues and concerns.

Course Outline

Detailed content for the course includes:

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.

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.

Module 3: AI development life cycle

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

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.

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.

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.

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.

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