Understanding AI Bias: Real-World Fairness Case Studies

Artificial intelligence shapes critical decisions in healthcare, hiring, and criminal justice, but these systems often carry hidden biases. Understanding how algorithmic bias manifests in the real world is the first step toward building more equitable technology. In this written course, you will explore real-world case studies to understand how AI fairness issues occur and learn how to identify bias in automated systems. You will transition from recognizing algorithmic disparities to understanding the modern frameworks used to evaluate and mitigate them. What you'll learn: - Understand the core concepts of algorithmic bias, fairness, and data representation. - Analyze real-world case studies in healthcare, recruitment, and risk assessment to see where AI systems failed. - Evaluate common fairness metrics, including demographic parity and equalized odds, through clear written explanations. - Explore how bias affects modern large language models and generative AI systems. - Learn foundational strategies and governance frameworks to mitigate bias during data collection and model training. You will begin by learning key terminology and the socio-technical factors that lead to biased data. From there, you will read through detailed, real-world case studies and practice identifying fairness issues using written scenarios and conceptual exercises. This course is designed for beginners, including aspiring data professionals, product managers, and tech enthusiasts, with no prior programming or advanced mathematics background required. Start reading today to build a foundational understanding of ethical AI practices.