Machine Learning Fairness: Identifying and Mitigating AI Bias

As machine learning models increasingly automate critical decisions from hiring to financial lending, ensuring these systems are fair and unbiased is more important than ever. Unchecked data bias can lead to discriminatory outcomes and damage trust in technology. This course guides you through the foundational concepts of AI ethics, data fairness, and bias mitigation. You will understand how bias enters data pipelines, how to measure it, and how to apply practical strategies to build more equitable models. What you'll learn: Understand the core definitions of fairness, equity, and bias in artificial intelligence; Identify common sources of data bias, from historical inequalities to sampling errors; Apply modern fairness metrics to evaluate predictive models and detect disparate impact; Explore mitigation techniques across different stages of the machine learning pipeline; Analyze real-world case studies in automated decision-making to identify ethical risks; Address modern challenges, including bias in large language models and generative AI. Starting with fundamental definitions of algorithmic fairness, you will progress through practical evaluation methods and mitigation frameworks. The text-only format allows you to study detailed explanations and conceptual code snippets at your own pace. This course is designed for beginners, aspiring data scientists, and tech professionals who want to understand AI ethics, with no prior advanced mathematics background required. Begin your journey toward building responsible and trustworthy AI today.