Classification Metrics for AI Fairness

Building machine learning models is only half the battle; ensuring they are fair, unbiased, and reliable is critical for responsible real-world deployment. This text-based course guides you through the essential classification metrics needed to audit AI systems for bias, helping you transition from basic model evaluation to ethical AI assessment. What you'll learn: - Understand the core concepts of AI bias and why standard performance metrics can sometimes mask systemic inequalities. - Deconstruct the confusion matrix to calculate accuracy, precision, recall, and F1-score from first principles. - Analyze model performance across diverse demographic groups using modern fairness criteria like demographic parity and equalized odds. - Identify the inherent trade-offs between overall model accuracy and ethical fairness in predictive systems. - Apply evaluation frameworks to written scenario analyses to detect and address bias in automated decision-making. You will start with foundational definitions of classification errors and basic terminology before exploring mathematical fairness formulations and practical evaluation strategies through clear, structured text and step-by-step calculations. This course is designed for aspiring data scientists, AI practitioners, and product managers who want a clear, beginner-friendly introduction to ethical model evaluation with no advanced prerequisites. Start reading today to build more equitable and transparent AI systems.