This practical guide operationalizes responsible AI by offering concrete guidelines for decision-makers and technologists on governance, design, and building trustworthy AI systems. It addresses governance mechanisms at various levels—industry, organizational, and team—alongside software engineering best practices, architectural styles, design patterns, and system-level techniques that connect code with data and models. The focus is on ensuring that AI systems are trustworthy throughout their lifecycle, instilling confidence in users. The guide not only covers the AI components but also the broader software infrastructure that supports them. It is the first to comprehensively address operationalizing responsible AI within the entire software development lifecycle, providing actionable insights on governance, process best practices, design patterns, and engineering techniques. Authored by leading experts in responsible technology and AI engineering, it aims to reduce risks associated with AI adoption while promoting ethical principles in products and policies. An online repository offers up-to-date patterns, techniques, examples, and playbooks. Real-world case studies illustrate responsible AI in action, making this a definitive resource for navigating the path to responsible AI excellence from governance to design.
