AIGP – TRAINING (EN)

This training teaches 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.

Understand the technological foundations of artificial intelligence 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.

This training teaches 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.

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.

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.

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