Responsible AI: Fairness and Bias Mitigation for Developers

As artificial intelligence becomes deeply integrated into everyday applications, building ethical and unbiased systems is no longer optional for software engineers. This course provides developers with a clear, practical pathway to understanding and implementing responsible AI practices. You will transition from simply training models to engineering fair, transparent, and accountable AI systems. Through comprehensive text-based explanations and code-focused walkthroughs, you will learn how to detect hidden biases in training datasets and apply modern algorithmic mitigation techniques to both traditional machine learning and modern generative models. What you'll learn: 1. Understand foundational ethical principles and the core components of responsible AI. 2. Identify different sources of bias in datasets, features, and model architectures. 3. Apply mathematical fairness metrics to evaluate model predictions and performance. 4. Implement pre-processing, in-processing, and post-processing bias mitigation techniques. 5. Evaluate modern generative AI models and large language models for safety, toxicity, and alignment. 6. Establish responsible development workflows and model monitoring strategies for production systems. The course starts with essential terminology and ethical frameworks before guiding you through data auditing, practical bias mitigation algorithms, and safety evaluations for advanced AI systems. You will read structured explanations, analyze Python code snippets, and complete written review exercises to solidify your understanding. This program is designed for software developers, data scientists, and technical product managers who are new to AI ethics. No prior experience with responsible AI is required, though a basic understanding of programming concepts is helpful. Start reading today to build AI systems that are fair, transparent, and trustworthy.