Overview

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This course is for learners who want a foundation in AI governance and compliance, including professionals who oversee or support AI use in their organizations. It begins by defining artificial intelligence, tracing its evolution from early concepts to large language models, and weighing its potential benefits against its risks, including documented AI failures. It then defines AI governance and compliance and explains the core goals governance seeks to protect, such as safety, security, fairness, accountability, transparency, and privacy. Learners next study how to operationalize governance through its objectives and objects, using the AI lifecycle, governance targets, and four models of governance. The course closes by contrasting a governance mindset with a compliance mindset and showing why AI systems need continuous, feedback-driven oversight.

Syllabus

Module 1: Understanding AI and the Need for Governance
- You will learn how artificial intelligence is defined, from Turing's early test to machine learning and modern definitions, and how the technology has evolved from early chatbots and expert systems to today's large language models. You will also learn about the potential benefits of AI, including its use in finance, and its risks, such as concentration of wealth, over-reliance, loss of control, and real-world failures. Finally, you will learn why AI governance and compliance are needed and what goals they serve, including safety, security, fairness, accountability, transparency, and privacy. Optionally, you can explore the NIST Glossary as a reference for AI terminology.
Module 2: Objectives and Objects of AI Governance
- You will learn how AI governance and compliance are defined, why governance gaps lead to real-world harm, and how frameworks such as the OECD principles, the Hiroshima AI Process, and the Council of Europe Convention shape the field. You will also learn how to operationalize governance through its objectives, including responsible AI, social license, and the minimalist-to-maximalist continuum, and through its objects, including the AI lifecycle stages and technical, human, process, and output targets, along with four models for understanding governance. Finally, you will learn how a governance mindset differs from a compliance mindset and why AI systems call for continuous, feedback-driven oversight.

Taught by

Brian Trackman

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