Responsible AI : best practices for creating trustworthy AI systems /
Lu, Qinghua,
Responsible AI : best practices for creating trustworthy AI systems / Responsible artificial intelligence Qinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu. - xix, 291 pages : illustrations ; 23 cm
Includes bibliographical references and index.
Preface -- About the author -- Part I. Background and introduction: 1. Introduction to responsible AI ; 2. Operationalizing responsible AI : A thought Experiment - Robbie the Robot -- Part II.: Responsible AI Pattern Catalogue:- 3. Overview of the Responsible AI Pattern Catalogue ; 4. Multi-Level Governance Patterns for Responsible AI ; 5. Process Patterns for Trustworthy Development Processes ; 6. Product Patterns for Responsible-AI-by-Design ; 7. Pattern-Oriented Reference Architecture for Responsible-AI-by-Design ; 8. Principle-Specific Techniques for Responsible AI -- Part III.: Case Studies: 9. Risk-Based AI Governance in Telstra ; 10. Reejig : The World's First Independently Audited Ethical Talent AI ; 11. Diversity and Inclusion in Artificial Intelligence -- Part IV. Looking to the Future: 12. The Future of Responsible AI ; Part V : Appendix -- Index.
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. -- Provided by publisher.
9780138073923 0138073929
2023915722
Artificial intelligence--Moral and ethical aspects.
Artificial intelligence.
Machine learning.
Intelligence artificielle--Aspect moral.
Intelligence artificielle.
Apprentissage automatique.
artificial intelligence.
Q334.5 / L83 2024
Responsible AI : best practices for creating trustworthy AI systems / Responsible artificial intelligence Qinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu. - xix, 291 pages : illustrations ; 23 cm
Includes bibliographical references and index.
Preface -- About the author -- Part I. Background and introduction: 1. Introduction to responsible AI ; 2. Operationalizing responsible AI : A thought Experiment - Robbie the Robot -- Part II.: Responsible AI Pattern Catalogue:- 3. Overview of the Responsible AI Pattern Catalogue ; 4. Multi-Level Governance Patterns for Responsible AI ; 5. Process Patterns for Trustworthy Development Processes ; 6. Product Patterns for Responsible-AI-by-Design ; 7. Pattern-Oriented Reference Architecture for Responsible-AI-by-Design ; 8. Principle-Specific Techniques for Responsible AI -- Part III.: Case Studies: 9. Risk-Based AI Governance in Telstra ; 10. Reejig : The World's First Independently Audited Ethical Talent AI ; 11. Diversity and Inclusion in Artificial Intelligence -- Part IV. Looking to the Future: 12. The Future of Responsible AI ; Part V : Appendix -- Index.
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. -- Provided by publisher.
9780138073923 0138073929
2023915722
Artificial intelligence--Moral and ethical aspects.
Artificial intelligence.
Machine learning.
Intelligence artificielle--Aspect moral.
Intelligence artificielle.
Apprentissage automatique.
artificial intelligence.
Q334.5 / L83 2024