This module explores the critical role of ethics in AI deployment, focusing on how values like fairness, accountability, and autonomy influence system design and outcomes. You’ll examine real-world dilemmas and learn how ethical principles can guide responsible decision-making in both public and private sector AI use.

Even well-intentioned AI systems can fail. When they do, the impact can be widespread and serious. This module explores the technical and organisational reasons behind AI failure, from algorithmic bias and hallucination to overreliance, poor data governance, and blind spots in leadership and oversight.

This module introduces the Trustworthy AI Cycle, a practical governance framework designed to ensure that AI systems are not just technically robust, but ethically sound and socially aligned. You’ll learn how to turn high-level principles into measurable practices across the AI lifecycle: from risk anticipation and data quality to testing, documentation, and ongoing monitoring.

This module explores how to implement AI responsibly within organisational settings, weighing the strategic decision to build or buy against governance, risk, and long-term value. You’ll learn how to embed AI into enterprise risk management, apply guardrails, and use practices like red teaming and the Three Lines of Defence to ensure trust, accountability, and operational readiness.

This final module brings together everything you’ve learned about ethical foundations, system failures, governance, and implementation strategies. You’ll consolidate your understanding by examining how organisations can align AI deployment with trust, accountability, and long-term value—and reflect on how these lessons apply to a business idea generated by AI.