Identifying and Mitigating Data Bias in AI Applications

Artificial intelligence increasingly shapes key decisions in our society, but underlying data bias can lead to unfair and harmful outcomes. Understanding how these biases enter our systems is the first step toward building responsible, ethical technology. This text-based course equips you with the fundamental knowledge to identify, measure, and mitigate data bias in modern AI applications. You will transition from recognizing societal impacts to understanding practical evaluation frameworks that support fairness. What you'll learn: Understand the core types of data bias, including historical, representation, and measurement bias; Analyze the societal and ethical consequences of biased algorithmic systems; Apply modern fairness metrics to evaluate machine learning models for disparate impact; Implement mitigation strategies during data collection and preprocessing; Explore bias considerations in modern large language models and generative AI systems; Practice designing ethical AI guidelines and audit frameworks for your projects. You will start by mastering key terminology, ethical principles, and foundational definitions before moving on to practical methodologies for analyzing data and model behavior. Through written case studies and conceptual exercises, you will learn how to design fairer systems from the ground up. This course is designed for beginners and requires no prior programming or advanced mathematics background. Start reading today to help build AI applications that are fair, transparent, and accountable.