By the end of this learning experience, learners should be able to:
Define artificial intelligence (AI) using generally accepted terminology and distinguish between major types of AI, such as narrow AI, general AI, generative AI, machine learning, and autonomous systems.
Recognise and explain the risks and harms of AI for individuals, groups, organisations, and society, including bias and discrimination, privacy violations, safety failures, misuse, misalignment with intended objectives, and challenges related to complexity and scalability.
Describe the characteristics that make AI distinct from traditional technologies and explain why these characteristics require a comprehensive governance approach, including opacity, autonomy, speed, scale, data dependency, probabilistic outputs, and potential for unintended harm or misuse.
Identify the core principles of responsible AI and explain their importance, including fairness, safety and reliability, privacy and security, transparency and explainability, accountability, and human-centred design.
Apply responsible AI principles in practical contexts by assessing AI systems, identifying governance needs, and evaluating whether AI use aligns with ethical, legal, and organizational expectations.
The IIA's Three Lines Model and emerging AI governance frameworks (e.g., NIST AI RMF, ISO/IEC 42001, EU AI Act) converge on a common precondition for effective oversight: an organisation must first establish its expectations and then communicate them coherently across the enterprise.
Without this foundational layer, downstream controls, risk assessments, model validation, monitoring become disconnected gestures rather than an integrated system.
This section aims to clarify how to govern AI development, emphasizing the responsibilities involved. Understanding AI governance underscores the key duties of professionals in this field, which include designing, building, training, testing, and maintaining AI systems with careful attention and dedication.
In this section, we focus on understanding how to govern AI deployment and use, emphasizing the responsibilities of AI governance professionals. This includes tasks such as selecting an AI model, deploying it, and using it responsibly through ongoing monitoring, maintenance, and other key duties. This domain applies to any deployment context, whether a company is using its own proprietary model or one from a third party.