Responsible AI: Global Risks, Governance, and Human Oversight

As AI systems become widely deployed and increasingly influential, organizations and individuals face growing risks—from model failures and bias to regulatory conflicts and misinformation. Understanding how to navigate these challenges requires more than technical knowledge.

In this course, get a practical, globally informed overview in AI risks, governance models, and responsible use. Using real examples, you'll learn to recognize failure patterns, assess explainability gaps and data privacy risks, and maintain human oversight in high-stakes decisions. Along the way, develop the literacy to know when not to use AI tools and how to evaluate AI systems against global governance standards.

This course features Professor Arisa Ema of the University of Tokyo—one of Japan's leading experts on AI governance and a renowned contributor to national AI policy councils.

Learning objectives
Identify and evaluate major AI risks including bias, privacy breaches, hallucinations, adversarial attacks, and robustness failures.
Compare governance models across the EU, US, Japan, and international organizations, understanding their implications for global AI deployment.
Apply practical responsible-AI practices within their organization, including transparency, explainability, and human oversight.
Recognise when AI should not be used and how to avoid overdependence in both personal and professional contexts.
Build foundational AI literacy needed to interpret AI outputs, question assumptions, and maintain integrity in decision-making.