Identifying and Mitigating Bias in the AI Life Cycle

Artificial intelligence shapes our world, but hidden biases in data and algorithms can lead to unfair and discriminatory outcomes. Understanding where and how bias creeps into AI systems is the first step toward building technology that everyone can trust. This text-based course guides you through the entire AI life cycle—from data collection to model deployment—showing you how to spot ethical risks and implement practical mitigation strategies. You will move from a basic understanding of algorithmic fairness to confidently identifying bias in modern AI systems, including large language models and automated decision pipelines. What you'll learn: - Understand foundational concepts of algorithmic fairness, ethics, and the socio-technical nature of AI bias - Identify how bias enters the AI life cycle during data collection, labeling, and preprocessing stages - Apply quantitative fairness metrics to evaluate model predictions and recognize disparate impact - Mitigate bias using pre-processing, in-processing, and post-processing algorithmic techniques - Evaluate modern generative AI systems and large language models for representational and occupational bias - Implement documentation frameworks like model cards and data sheets to ensure transparency and accountability Starting with essential terminology and ethical foundations, you will progress through realistic scenarios that illustrate how data choices impact real-world outcomes. You will read through clear explanations of fairness metrics and explore modern standards for documenting AI models responsibly. This course is designed for beginners, aspiring data scientists, product managers, and tech enthusiasts who want to build ethical AI. No prior programming or advanced mathematics background is required. Start reading today to champion fairness and transparency in the future of technology.