Responsible AI : Best Practices for Creating Trustworthy AI Systems PDF

THE FIRST PRACTICAL GUIDE FOR OPERATIONALIZING RESPONSIBLE AI˜FROM MUL TI(deg)LEVEL GOVERNANCE MECHANISMS TO CONCRETE DESIGN PATTERNS AND SOFTWARE ENGINEERING TECHNIQUES.

AI is solving real-world challenges and transforming industries. Yet, there are serious concerns about its ability to behave and make decisions in a responsible way. Operationalizing responsible AI is about providing concrete guidelines to a wide range of decisionmakers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organizational, and team level; software engineering best practices; architecture styles and design patterns; system-level techniques connecting code with data and models; and trade-offs in design decisions. Responsible AI includes a set of practices that technologists (for example, technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure the AI systems they develop or adopt are trustworthy throughout the entire lifecycle and can be trusted by those who use them. The book offers guidelines and best practices not just for the AI part of a system, but also for the much larger software infrastructure that typically wraps around the AI.

• First book of its kind to cover the topic of operationalizing responsible AI from the perspective of the entire software development life cycle.
• Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system engineering techniques.
• Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering.
• Reduce the risks of AI adoption, accelerate AI adoption in responsible ways, and translate ethical principles into products, consultancy, and policy impact to support the AI industry.
• Online repository of patterns, techniques, examples, and playbooks kept up-to-date by the authors.
• Real world case studies to demonstrate responsible AI in practice.
• Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this defi nitive guide.