Skip to Main Content

IS461

Download as PDF

AI Governance

SCIS Sch of Computing & Info Sys

Course (UG/PG)

Undergraduate

Offering Unit/Department

SCIS

Course Description

Building deployable AI systems for high-stakes domains—healthcare, finance, public services—requires engineers to navigate institutional, regulatory, and human constraints from the start. This course teaches AI Governance as a technical discipline, focusing on the engineering, evaluation, and operational requirements that make AI systems safe, auditable, and trustable at scale.Students learn how policies and standards—such as the EU AI Act, ISO/IEC 42001, NIST AI RMF, and Singapore's Model AI Governance Framework—translate into concrete system requirements, architectural choices, testing protocols, documentation artefacts, and monitoring workflows.Grounded in real-world constraints from multilateral governance and public-sector deployments, the course covers:* Governance-as-Engineering: Developing practical artefacts such as risk registers, model /system cards, and evaluation and monitoring plans. Infrastructure-Scale Data Governance: Architecting for secure data access, privacy-enhancing technologies (PETs), and compliance in multi-stakeholder environments—as exemplified by Digital Public Infrastructure (DPI).* Behavioural Trust Design: Engineering for “felt governance”: designing explanations, recourse pathways, and user-facing signals that shape perceived safety, fairness, and control.* Cross-Functional Translation: Bridging engineering, product, operations, legal, and compliance requirements into coherent system design.The course provides students with exposure to practical methods used in AI governance and offers opportunities to create selected governance artefacts for learning purposes. These outputs are intended to support understanding of industry practices, without implying professional qualification or job preparation.

Course Learning Outcomes

Course Objectives

  1. Describe key concepts, principles, and frameworks related to AI governance in the current data economy.

  2. Identify common governance challenges that may arise across different stages of the AI/ML lifecycle.

  3. Explain fundamental considerations related to data governance, secure data use, and the management of data assets.

  4. Apply selected AI governance concepts to analyse simple case examples in areas such as healthcare, finance, or public services.

  5. Summarise how regulatory, organisational, or infrastructural requirements may influence basic system design decisions.

  6. Recognise the types of documentation and processes commonly used in AI governance, and explain their purpose at a basic level.

Competencies

  1. Basic concepts related to AI governance and responsible AI.

  2. Introductory awareness of how data governance and data management practices support AI systems.

  3. General understanding of the types of risks and considerations that may arise across the AI/ML lifecycle.

  4. Familiarity with high-level regulatory and organisational factors that influence AI deployment.

  5. Foundational insight into the documentation and processes commonly referenced in AI governance discussions.

Discipline-Specific Competencies

Algorithmic thinking, Business Innovation, Design Thinking Practice, Emerging Technology Synthesis, Problem-solving & analysis

SMU Graduate Learning Outcomes

Disciplinary knowledge, Interdisciplinary knowledge, Multidisciplinary knowledge, Critical thinking & problem solving, Innovation & enterprising skills, Collaboration and leadership, Understanding of global and Asian perspectives, Understanding of sustainability issues, Resilience

Grading Basis

GRD - Graded

Course Units

1