Fairness in AI: Identifying and Mitigating Algorithmic Bias

Artificial intelligence systems increasingly shape critical decisions in hiring, lending, and healthcare, yet they often inherit and amplify human biases. Understanding how algorithms become biased and how to correct them is essential for building ethical, trustworthy technology. In this text-only course, you will transition from understanding foundational ethical concepts to actively measuring and mitigating bias in machine learning workflows. What you'll learn: • Understand the core terminology of AI ethics, systemic bias, and historical data discrimination. • Analyze how bias enters the machine learning lifecycle through data collection and model training. • Measure algorithmic fairness using key mathematical metrics such as demographic parity and equalized odds. • Apply modern mitigation techniques, including pre-processing, in-processing, and post-processing algorithms. • Evaluate AI models for bias using modern auditing frameworks and ethical guidelines. • Explore the unique fairness challenges in large language models and generative AI systems. The course begins with foundational definitions of bias and discrimination before walking you through hands-on, text-based scenarios. You will read detailed explanations, analyze code snippets for bias detection, and practice applying mitigation strategies through written exercises. This course is designed for beginners, including aspiring data scientists, product managers, and tech ethicists who want to understand algorithmic fairness. No prior advanced mathematics or programming experience is required. Start reading today to build AI systems that are fair, transparent, and accountable.