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 levels Development process perspectives including software engineering
best practices for AI System perspectives including quality attributes architecture styles
and patterns Techniques for connecting code with data and models including key tradeoffs
Principle-specific techniques for fairness privacy and explainability A preview of the future
of responsible AI
