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.

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.

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

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

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

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

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

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