RESPONSIBLE AI

AI systems are solving real world challenges and transforming industries but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them Many ethical principles and guidelines have been proposed for AI systems but they re often too high level to be translated into practice Conversely AI ML researchers often focus on algorithmic solutions that are too low level to adequately address ethics and responsibility In this timely practical guide pioneering AI practitioners bridge these gaps The authors illuminate issues of AI responsibility across the entire system lifecycle and all system components offer concrete and actionable guidance for addressing them and demonstrate these approaches in three detailed case studies Writing for technologists decision makers students users and other stake holders the topics cover Governance mechanisms at industry organisation and team levelsDevelopment process perspectives including software engineering best practices for AISystem perspectives including quality attributes architecture styles and patternsTechniques for connecting code with data and models including key tradeoffsPrinciple specific techniques for fairness privacy and explainabilityA preview of the futu