AI Fairness: Recognizing and Preventing Data Bias

As artificial intelligence increasingly shapes our daily lives, ensuring these systems are fair and unbiased is more critical than ever. Unchecked data bias can lead to discriminatory outcomes, making ethical AI design a vital skill for modern technology professionals. This text-only course guides you through the foundational concepts of algorithmic fairness, helping you transition from a beginner to a practitioner capable of identifying and addressing bias in data pipelines. You will read detailed explanations, analyze realistic case studies, and work through conceptual exercises designed to build your ethical decision-making framework. What you'll learn: Understand the fundamental types of data bias, including historical, representation, and measurement bias; Identify bias in modern generative AI models and large language model datasets; Apply key fairness metrics such as demographic parity and equalized odds to evaluate machine learning models; Implement data preprocessing and post-processing mitigation techniques to reduce algorithmic unfairness; Analyze real-world ethical case studies to establish best practices for responsible AI development. The course begins with core ethical definitions and foundational concepts of data collection before moving into practical metrics and strategic mitigation workflows. You will progress systematically from theory to application through written explanations and structured analytical exercises. Designed specifically for beginners, this course requires no prior background in advanced mathematics or machine learning to get started. Begin reading today to build fairer, more responsible technology for tomorrow.