Identifying and Evaluating Data Bias in AI Systems

Artificial intelligence shapes modern decision-making, but biased data can lead to unfair, inaccurate, and harmful automated outcomes. Understanding how bias enters AI systems is the first critical step toward building responsible technology. In this text-based course, you will learn how to spot hidden biases in datasets, measure their impact on machine learning models, and apply practical evaluation strategies. You will move from a basic understanding of algorithmic fairness to confidently auditing data for ethical AI development. Through clear, written explanations, you will learn to: 1. Understand the core concepts of data bias, its common sources, and its impact on modern AI systems. 2. Identify different types of bias, including sampling, historical, and algorithmic bias, within datasets. 3. Apply standard fairness metrics to evaluate model predictions and detect disparate impact. 4. Learn modern mitigation techniques to balance datasets and reduce bias during the preprocessing phase. 5. Explore modern AI governance frameworks and best practices for auditing generative AI models. The course begins with foundational terminology and the ethics of data collection, before guiding you through practical evaluation techniques and structured auditing workflows. This beginner-friendly course is designed for aspiring data professionals, product managers, and tech enthusiasts, with no prior programming or advanced statistics experience required. Read through the structured lessons and start building fairer AI systems today.