AI systems increasingly influence decisions that affect people, organizations, and society. But how can you identify when those systems are unfair, and what can you do about it?

In this course, you will explore the concept of bias in AI systems and its relationship to fairness. You’ll learn how unwanted bias can arise from data, human decision-making, and engineering choices, and how these factors can impact AI outcomes in areas such as hiring, healthcare, finance, and security. Through practical examples, you will examine fairness metrics including confusion matrices, equalized odds, equality of opportunity, demographic parity, and predictive equality, gaining the skills to assess and evaluate bias in AI systems. You will also explore proven strategies for controlling and mitigating bias throughout the AI system life cycle, from inception and design to deployment and ongoing monitoring. What makes this course unique is its combination of internationally recognized guidance from ISO/IEC TR 24027:2021 with practical assessment techniques, real-world case studies, fairness measurement methods, and life cycle-based mitigation strategies. By the end of the course, you will be equipped to identify bias risks, evaluate fairness, and contribute to the development of more trustworthy, transparent, and accountable AI systems.