Responsible AI for Developers: Fairness, Bias, and Ethics

As AI systems increasingly influence critical real-world decisions, developers must ensure these technologies are fair, unbiased, and ethical. Building responsible AI is no longer optional; it is a fundamental requirement for modern software engineering. This text-only course guides you through the core principles of AI ethics, helping you transition from conceptual understanding to practical implementation. You will learn to recognize different types of bias, apply evaluation techniques, and establish guardrails to safeguard your models. What you'll learn: - Understand the foundational definitions of responsible AI, fairness, and systemic bias. - Identify common sources of bias in training data, algorithms, and model outputs. - Apply modern fairness metrics to evaluate machine learning model predictions. - Implement mitigation techniques to minimize bias during data preparation and model training. - Explore modern safety alignment practices, including red-teaming and prompt guardrails for large language models. - Establish clear ethical workflows to monitor and maintain model fairness post-deployment. The course begins with foundational ethical principles and terminology before guiding you through hands-on scenarios, data analysis techniques, and practical mitigation strategies. This course is designed for software developers, data scientists, and technical product managers who are new to AI ethics and want to build more equitable technology. No advanced mathematics or prior machine learning background is required. Start reading today to build AI systems that everyone can trust.