Evaluating AI Fairness with a Space University Scenario

As machine learning models increasingly make life-altering decisions, ensuring these algorithms are fair and unbiased is more critical than ever. But how do we translate the abstract concept of fairness into concrete, mathematical metrics? This text-only course guides you through the core principles of AI ethics, using an engaging simulated admissions scenario for a diverse, multi-species university to make complex ideas highly intuitive. You will learn how to audit predictive models, identify systemic bias, and apply industry-standard fairness criteria to real-world decision-making systems. What you'll learn: - Understand the foundational definitions of bias, equity, and fairness in machine learning systems. - Calculate key mathematical fairness metrics, including demographic parity, disparate impact, and equalized odds. - Analyze the trade-offs between model accuracy, selection quality, and equal opportunity. - Identify sources of historical and algorithmic bias within training datasets. - Apply conceptual mitigation strategies to adjust model outcomes and ensure equitable decisions. This course begins with essential terminology and the philosophical underpinnings of algorithmic fairness. Next, you will explore the simulated admissions case study, reading through step-by-step explanations, conceptual breakdowns, and written exercises that show how to measure and balance fairness in practice. Designed for beginners, this course requires no advanced mathematical or programming background. Start learning how to evaluate and build more ethical, transparent AI systems today.