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Course Outline

Introduction

Module 1: Foundations of Artificial Intelligence

  • Establishes the definitions of artificial intelligence and machine learning, provides an overview of various AI system types and their applications, and situates AI models within the broader socio-cultural landscape. Upon completion of this module, learners will be able to;
  • Describe and explain the distinctions among different AI systems.
  • Describe and explain the components of the AI technology stack.
  • Describe and explain the relationship between AI and the evolution of data science.

Module 2: AI Impacts on People and Responsible AI Principles

  • Identifies the core risks and potential harms associated with AI systems, outlines the attributes of trustworthy AI, and details the principles necessary for responsible and ethical AI deployment. Upon completion of this module, learners will be able to;
  • Describe and explain the primary risks and harms posed by AI systems.
  • Describe and explain the characteristics that define trustworthy AI systems.

Module 3: AI Development Life Cycle

  • Examines the lifecycle of AI development and the broader context for managing AI-related risks. Upon completion of this module, learners will be able to;
  • Describe and explain the similarities and differences among current and emerging ethical guidance regarding AI.
  • Describe and explain existing legal frameworks that interact with AI usage.
  • Describe and explain key intersections with the General Data Protection Regulation (GDPR).
  • Describe and explain developments in liability reform.

Module 4: Implementing Responsible AI Governance and Risk Management

  • Details how major stakeholders collaborate through a layered approach to mitigate AI risks while recognizing the potential societal benefits of these systems. Upon completion of this module, learners will be able to;
  • Describe and explain the requirements of the EU AI Act.
  • Describe and explain other emerging global legislation.
  • Describe and explain the similarities and differences among major risk management frameworks and standards.

Module 5: Implementing AI Projects and Systems

  • Outlines the processes for mapping, planning, and scoping AI projects; testing and validating systems during development; and managing and monitoring systems post-deployment. Upon completion of this module, learners will be able to;
  • Describe and explain the key steps in the AI system planning phase.
  • Describe and explain the key steps in the AI system design phase.
  • Describe and explain the key steps in the AI system development phase.
  • Describe and explain the key steps in the AI system implementation phase.

Module 6: Current Laws That Apply to AI Systems

  • Surveys existing legislation governing AI use, outlines key intersections with GDPR, and raises awareness regarding liability reform. Upon completion of this module, learners will be able to;
  • Ensure interoperability of AI risk management with other operational risk strategies.
  • Integrate AI governance principles into organizational practices.
  • Establish an effective AI governance infrastructure.
  • Map, plan, and scope the AI project.
  • Test and validate the AI system during the development process.
  • Manage and monitor AI systems following deployment.

Module 7: Existing and Emerging AI Laws and Standards

  • Describes global AI-specific regulations and major frameworks that illustrate responsible governance of AI systems. Upon completion of this module, learners will be able to;
  • Demonstrate awareness of legal issues related to AI.
  • Demonstrate awareness of user concerns regarding AI.
  • Demonstrate awareness of AI auditing and accountability issues.

Module 8: Ongoing AI Issues and Concerns

  • Presents current discussions and perspectives on AI governance, including awareness of legal issues, user concerns, and AI auditing and accountability matters.

Summary and Next Step

Requirements

No prior qualifications are required for this curriculum.

Target Audience

It is essential to establish and enhance the governance frameworks that enable trustworthy artificial intelligence, while simultaneously investing in the personnel responsible for developing ethical and responsible AI systems. Individuals employed in compliance, privacy, security, risk management, legal, human resources, and governance roles, as well as data scientists, AI project managers, business analysts, AI product owners, model operations teams, and other relevant stakeholders, must be equipped to address the expanded equities associated with AI governance.

This training is designed for professionals responsible for implementing AI governance and risk management within their organizations, as well as candidates seeking IAPP Artificial Intelligence Governance Professional (AIGP) certification for government applications.

 28 Hours

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