Responsible AI: Best Practices for Creating Trustworthy AI Systems (Early Release)

Cover Page
Halftitle Page
Title Page
Copyright Page
Pearson’s Commitment to Diversity, Equity, and Inclusion
Dedication Page
Contents
Table of Contents
Preface
Acknowledgments
About the Author
Part I: Background and Introduction
   1. Introduction to Responsible AI
      What Is Responsible AI?
      What Is AI?
      Developing AI Responsibly: Who Is Responsible for Putting the “Responsible” into AI?
      About This Book
      How to Read This Book
   2. Operationalizing Responsible AI: A Thought Experiment—Robbie the Robot
      A Thought Experiment—Robbie the Robot
      Summary
Part II: Responsible AI Pattern Catalogue
   3. Overview of the Responsible AI Pattern Catalogue
      The Key Concepts
      Why Is Responsible AI Different?
      A Pattern-Oriented Approach for Responsible AI
   4. Multi-Level Governance Patterns for Responsible AI
      Industry-Level Governance Patterns
      Organization-Level Governance Patterns
      Team-Level Governance Patterns
      Summary
   5. Process Patterns for Trustworthy Development Processes
      Requirements
      Design
      Implementation
      Testing
      Operations
      Summary
   6. Product Patterns for Responsible-AI-by-Design
      Product Pattern Collection Overview
      Supply Chain Patterns
      System Patterns
      Operation Infrastructure Patterns
      Summary
   7. Pattern-Oriented Reference Architecture for Responsible-AI-by-Design
      Architectural Principles for Designing AI Systems
      Pattern-Oriented Reference Architecture
      Summary
   8. Principle-Specific Techniques for Responsible AI
      Fairness
      Privacy
      Explainability
      Summary
Part III: Case Studies
   9. Risk-Based AI Governance in Telstra
      Policy and Awareness
      Assessing Risk
      Learnings from Practice
      Future Work
   10. Reejig: The World’s First Independently Audited Ethical Talent AI
      How Is AI Being Used in Talent?
      What Does Bias in Talent AI Look Like?
      Regulating Talent AI Is a Global Issue
      Reejig’s Approach to Ethical Talent AI
      How Ethical AI Evaluation Is Done: A Case Study in Reejig’s World-First Independently Audited Ethical Talent AI
      Project Overview
      The Ethical AI Framework Used for the Audit
      The Benefits of Ethical Talent AI
      Reejig’s Outlook on the Future of Ethical Talent AI
   11. Diversity and Inclusion in Artificial Intelligence
      Importance of Diversity and Inclusion in AI
      Definition of Diversity and Inclusion in Artificial Intelligence
      Guidelines for Diversity and Inclusion in Artificial Intelligence
      Conclusion
Part IV: Looking to the Future
   12. The Future of Responsible AI
      Regulation
      Education
      Standards
      Tools
      Public Awareness
      Final Remarks
Part V: Appendix
   Governance Patterns
   Process Patterns
   Product Patterns
   Principle-Specific Techniques