Sources of Bias & Fairness Basics

This lesson introduces the concept of bias in AI systems and explains how bias can emerge during data collection, labeling, measurement, and model development. Learners explore major bias categories including sampling bias, label bias, measurement bias, and proxy bias. The lesson also provides an introduction to AI fairness, demographic parity, equalized odds, and the challenges organizations face when balancing fairness, performance, and business objectives.