AI Fairness Fundamentals: Identifying and Mitigating Algorithmic Bias

As artificial intelligence increasingly shapes decision-making in hiring, finance, and healthcare, building equitable systems is a critical necessity. This course provides a clear, practical pathway to understanding how algorithmic bias occurs, how to measure it, and how to fix it. You will transition from a general understanding of AI to a practical framework for auditing algorithms for fairness. Through structured text lessons and conceptual exercises, you will learn to recognize bias in training data, apply quantitative fairness metrics, and implement modern mitigation strategies to ensure your models treat all demographic groups equitably. What you will learn: 1. Understand the core definitions, sources, and real-world impacts of algorithmic bias. 2. Identify how historical and systemic biases creep into dataset collection and labeling. 3. Apply mathematical fairness metrics such as demographic parity and equalized odds. 4. Implement pre-processing, in-processing, and post-processing mitigation techniques. 5. Explore modern generative AI ethics, including addressing bias in large language models. 6. Establish continuous monitoring practices to detect bias drift in production systems. The course begins with foundational concepts of ethics and data equity, then moves step-by-step through practical audit techniques, metrics, and mitigation workflows. This course is designed for aspiring data scientists, software developers, and product managers who are new to AI ethics and want to build responsible technology. Start reading today to champion fairness in your next machine learning project.