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 Duration 28 hours

Course Outline

Preamble

Module 1: Fundamental Principles of Artificial Intelligence

  • This section defines artificial intelligence and machine learning, providing a comprehensive overview of diverse AI system architectures and their applications within public sector contexts. It situates AI models within the broader sociocultural framework. Upon completion, participants will be able to:
  • Identify and distinguish between various categories of AI systems.
  • Elucidate the components of the AI technology stack.
  • Examine the relationship between AI and the evolution of data science.

Module 2: Societal Impacts and Principles of Responsible AI

  • This section outlines the primary risks and potential harms associated with AI systems, details the attributes of trustworthy AI, and establishes the principles requisite for responsible and ethical AI adoption. Upon completion, participants will be able to:
  • Identify and analyze the core risks and harms presented by AI systems.
  • Explain the defining characteristics of trustworthy AI systems.

Module 3: The AI Development Life Cycle

  • This section describes the AI development life cycle and the regulatory context in which AI risks are governed. Upon completion, participants will be able to:
  • Analyze similarities and differences among existing and emerging ethical guidance regarding AI.
  • Identify existing statutory frameworks that intersect with AI utilization.
  • Examine key intersections with GDPR requirements.
  • Discuss liability reform considerations.

Module 4: Execution of Responsible AI Governance and Risk Management

  • This section explains how principal stakeholders collaborate within a layered framework to manage AI risks, while recognizing the potential societal benefits of AI systems for government. Upon completion, participants will be able to:
  • Outline the requirements of the EU AI Act.
  • Review other emerging global legislative measures.
  • Compare major risk management frameworks and standards.

Module 5: Deployment of AI Projects and Systems

  • This section details the mapping, planning, and scoping of AI projects, as well as the testing, validation, and post-deployment monitoring of AI systems. Upon completion, participants will be able to:
  • Identify critical steps in the AI system planning phase.
  • Identify critical steps in the AI system design phase.
  • Identify critical steps in the AI system development phase.
  • Identify critical steps in the AI system implementation phase.

Module 6: Statutory Frameworks Applicable to AI Systems

  • This section surveys the existing laws governing AI use, highlights key GDPR intersections, and addresses liability reform. Upon completion, participants will be able to:
  • Ensure interoperability of AI risk management with other operational risk strategies.
  • Integrate AI governance principles into organizational operations.
  • Establish robust AI governance infrastructure.
  • Map, plan, and scope AI projects.
  • Test and validate AI systems during development.
  • Manage and monitor AI systems post-deployment.

Module 7: Existing and Emerging AI Laws and Standards

  • This section describes global AI-specific legislation and the major frameworks that exemplify responsible AI governance. Upon completion, participants will be able to:
  • Develop awareness of legal issues.
  • Develop awareness of user concerns.
  • Develop awareness of AI auditing and accountability issues.

Module 8: Persistent AI Issues and Concerns

  • This section presents current discussions and concepts regarding AI governance, including legal issues, user concerns, and accountability for government.

Summary and Subsequent Steps

Requirements

There are no prerequisites for this course.

Target Audience

Governments must continue to build and refine the governance processes through which trustworthy AI will emerge for government. There is an imperative to invest in the personnel responsible for building ethical and responsible AI. Professionals working in compliance, privacy, security, risk management, legal, HR, and governance, along with data scientists, AI project managers, business analysts, AI product owners, and model operations teams, must be prepared to address the expanded equity issues inherent in AI governance.

This includes any professionals tasked with developing AI governance and risk management in their operations, as well as individuals pursuing the IAPP Artificial Intelligence Governance Professional (AIGP) certification.

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