Responsible AI for Developers: Identifying and Mitigating Bias

As AI systems become more integrated into daily life, developers have a critical responsibility to ensure these technologies are fair, transparent, and accountable. This course provides a clear path for understanding how bias enters technical systems and how to systematically address it. You will move from understanding the theoretical foundations of AI ethics to applying practical techniques that measure and mitigate bias in datasets and models. By the end of this course, you will be equipped to evaluate the fairness of your machine learning workflows and implement standards that promote inclusivity. What you'll learn: - Understand the fundamental principles of responsible AI and common sources of algorithmic bias. - Apply statistical techniques to detect unfairness in training data and model predictions. - Implement bias mitigation strategies during data preprocessing, model training, and post-processing. - Explore modern transparency tools like Model Cards to document model behavior and limitations. - Address fairness challenges specific to large language models, including toxicity and representation. - Practice evaluating models using open-source fairness frameworks and cloud-based assessment tools. The course begins with essential terminology and ethical definitions before moving into technical implementation and modern documentation standards. You will read through detailed explanations and practical examples designed for immediate application. This course is designed for beginner developers and data scientists; no prior experience with AI ethics is required. Start building more accountable and inclusive technology today.