Operationalising Trustworthy AI | IATAI | Course 1, Course 1: Engineering Governance with Data, Traceability and Automation.
Course Description
Operationalising Trustworthy AI
Course 1: Engineering Governance with Data, Traceability & Automation
As AI systems become increasingly dynamic, organisations need new approaches to governance that go beyond documentation and periodic audits. This course explores how modern AI governance is evolving towards connected governance data, lifecycle traceability, automated evidence collection and continuous assurance.
You’ll learn how Trustworthy AI principles are translated into practical engineering processes using governance requirements, controls, evidence and assurance. Along the way, you’ll discover how governance data enables automation, improves auditability and supports trustworthy AI throughout the entire lifecycle.
Whether you’re an AI engineer, software engineer, architect, MLOps practitioner or governance professional, this course provides a practical introduction to engineering modern AI governance systems.
What you’ll learn
- Why traditional AI governance is evolving towards continuous assurance
- How governance data, metadata and traceability support Trustworthy AI
- The difference between governance, assurance, controls and evidence
- How evidence engineering enables automation and continuous compliance
- How Trustworthy AI principles are operationalised through engineering practice
- How modern organisations are embedding governance directly into AI development and MLOps workflows
This course is developed by the Irish Alliance for Trustworthy AI. The Irish Alliance for Trustworthy AI is a civic alliance of people and organisations working together to ensure AI is developed and used responsibly, inclusively and in the public interest.
