Responsible AI for Developers: Managing Bias and Fairness

As artificial intelligence becomes deeply integrated into everyday software, developers face the critical responsibility of ensuring these systems are fair, transparent, and unbiased. Building ethical AI is no longer optional—it is a core software engineering requirement. This text-based course equips you with the foundational knowledge and practical strategies needed to detect and address algorithmic bias in your machine learning workflows. You will transition from understanding abstract ethical principles to applying concrete fairness metrics and mitigation techniques in modern development environments. What you'll learn: - Understand the core principles of responsible AI, including fairness, accountability, and transparency. - Identify common sources of bias in training datasets and machine learning pipelines. - Apply quantitative fairness metrics to evaluate model performance across different demographic groups. - Implement bias mitigation techniques during pre-processing, in-processing, and post-processing phases. - Explore modern safety challenges in large language models (LLMs), including prompt safety and output alignment. - Establish best practices for documenting model cards and maintaining ethical data collection workflows. You will begin by mastering essential terminology and ethical frameworks before moving step-by-step through dataset auditing, model evaluation, and modern bias-reduction techniques. Through clear written explanations, practical code walk-throughs, and conceptual exercises, you will learn how to integrate fairness into every stage of the software development lifecycle. This course is designed for software developers, data scientists, and aspiring AI engineers who want to build ethical technology. No prior background in ethics or advanced statistics is required; a basic understanding of programming concepts is helpful. Start reading today to build AI systems that users can trust.