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 future of 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 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
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