This module explores practical approaches to ethical AI design, using the Stanford AI Ethics Toolkit to connect ethical principles with real-world decision-making. Learners will examine how design choices influence human autonomy, dignity, and relationships, while applying practical frameworks to evaluate AI systems in diverse contexts.

This module examines how AI systems are tested, evaluated, and governed to ensure safe and responsible deployment. Learners will explore risk-based approaches to testing, understand the relationship between evaluation and regulation, and analyse how technical, human, and organisational factors influence AI performance in high-stakes contexts.

This module explores how fairness can be operationalised across the AI lifecycle, from defining affected groups and identifying risks to applying technical and governance tools. Learners will examine fairness trade-offs, safeguards, and corrective actions for AI-supported decisions.

This module explores the principles of privacy, data protection, and data governance in AI, examining how responsible AI systems balance innovation with user rights, accountability, and trust. Learners will explore key governance concepts, practical tools, and real-world case studies to understand how privacy, transparency, human oversight, and responsible data practices support trustworthy AI throughout its lifecycle.

This module explores the principles of Transparency, Explainability, and Accountability (TEA) in AI systems. Learners will examine how TEA supports trust, human oversight, and responsible AI governance while developing practical skills to assess, communicate, and improve transparency and accountability throughout the AI lifecycle.

This module explores the environmental sustainability of AI systems, examining their impacts across the entire AI lifecycle. Learners will develop practical skills to identify environmental hotspots, evaluate sustainability trade-offs, and apply frameworks that support responsible decision-making and organisational sustainability practices.

This module explores proportionality and risk-based governance in AI, focusing on how to assess, manage, and mitigate risks while protecting fundamental rights and enabling innovation. Learners will apply practical assessment frameworks and governance tools to support responsible AI decision-making.

This module explores the impact of AI on human autonomy, meaningful work, and workplace wellbeing. Learners will learn how to design AI systems that support human agency, implement effective oversight, and promote ethical and responsible AI adoption in the workplace.

This module explores the complexities of AI governance across different countries, organisations, and regulatory contexts. Learners will delve into governance frameworks, cross-border challenges, and practical approaches to balancing innovation, accountability, and trust in AI systems.