Evaluating AI Fairness: A Guide to Predictive Parity

As machine learning models increasingly automate critical decisions, ensuring they treat all demographic groups fairly is a technical and ethical necessity. Without proper evaluation, automated systems can easily perpetuate and amplify historical biases. This text-only course guides you through the foundational concepts of AI fairness, focusing specifically on predictive parity—the metric that ensures equal precision across different groups. You will learn how to define, calculate, and implement this critical metric to detect and mitigate bias in predictive models. What you'll learn: - Understand the fundamental terminology of AI fairness and the definition of predictive parity. - Calculate predictive parity using confusion matrices and precision metrics across diverse demographic groups. - Compare predictive parity with other fairness criteria, such as demographic parity and equalized odds. - Analyze real-world scenarios where failing to meet predictive parity leads to systemic bias. - Apply modern bias-mitigation workflows to improve model equity and performance. - Practice evaluating classification models using structured evaluation metrics. You will start with core ethical definitions before moving step-by-step through mathematical formulations, comparative analysis of metrics, and practical mitigation workflows. Through clear written explanations and structured text exercises, you will build a solid framework for auditing AI systems. This course is designed for beginner data scientists, product managers, and technology ethics enthusiasts, with no advanced mathematical background required. Read along to start building fairer, more transparent AI systems today.