Fairness in AI: Detecting and Mitigating Algorithmic Bias

Artificial intelligence systems increasingly influence high-stakes decisions, making fairness and ethical design essential for modern technology practitioners. By understanding how bias enters datasets and algorithms, you can take concrete steps to evaluate and improve the equity of machine learning systems. What you'll learn: - Understand foundational definitions, ethical principles, and primary sources of algorithmic bias. - Detect subtle biases across training datasets and data collection pipelines. - Apply standard fairness metrics to evaluate classification and prediction systems. - Practice data preprocessing, in-processing, and post-processing mitigation techniques. - Evaluate bias and safety considerations in modern generative models and prompt systems. - Implement responsible governance principles and transparent model documentation. The course begins with core terminology and theoretical foundations before progressing through practical evaluation techniques and mitigation frameworks in written lessons. This material is designed for beginners, analysts, and aspiring practitioners with no prior background in AI ethics required. Begin reading today to help shape fair and responsible artificial intelligence.