RESPONSIBLE AI : best practices for creating trustworthy ai systems

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 Cover Half Title Title Page Copyright Page Contents Preface 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 Who Should Be Involved in Building Robbie? What Are the Responsible AI Principles for Robbie? Robbie and Governance Considerations Robbie and Process Considerations Robbie and Product Considerations Summary Part II: Responsible AI Pattern Catalogue 3 Overview of the Responsible AI Pattern Catalogue The Key Concepts The Multifaceted Meanings of Responsible Varied Understandings of Operationalization The Duality of Trust and Trustworthiness Why Is Responsible AI Different? A Pattern-Oriented Approach for Responsible AI 4 Multi-Level Governance Patterns for Responsible AI Industry-Level Governance Patterns G.1. RAI Law and Regulation G.2. RAI Maturity Model G.3. RAI Certification G.4. Regulatory Sandbox G.5. Building Code G.6. Independent Oversight G.7. Trust Mark G.8. RAI Standards Organization-Level Governance Patterns G.9. Leadership Commitment for RAI G.10. RAI Risk Committee G.11. Code of RAI G.12. RAI Risk Assessment G.13. RAI Training G.14. Role-Level Accountability Contract G.15. RAI Bill of Materials G.16. Standardized Reporting Team-Level Governance Patterns G.17. Customized Agile Process G.18. Tight Coupling of AI and Non-AI Development G.19. Diverse Team G.20. Stakeholder Engagement G.21. Continuous Documentation Using Templates G.22. Verifiable Claim for AI System Artifacts G.23. Failure Mode and Effects Analysis (FMEA) G.24. Fault Tree Analysis (FTA) Summary 5 Process Patterns for Trustworthy Development Processes Requirements P.1. AI Suitability Assessment P.2. Verifiable RAI Requirement P.3. Lifecycle-Driven Data Requirement P.4. RAI User Story Design P.5. Multi-Level Co-Architecting P.6. Envisioning Card P.7. RAI Design Modeling P.8. System-Level RAI Simulation P.9. XAI Interface Implementation P.10. RAI Governance of APIs P.11. RAI Governance via APIs P.12. RAI Construction with Reuse Testing P.13. RAI Acceptance Testing P.14. RAI Assessment for Test Cases Operations P.15. Continuous Deployment for RAI P.16. Extensible, Adaptive, and Dynamic RAI Risk Assessment P.17. Multi-Level Co-Versioning Summary 6 Product Patterns for Responsible-AI-by-Design Product Pattern Collection Overview Supply Chain Patterns D.1. RAI Bill of Materials Registry D.2. Verifiable RAI Credential D.3. Co-Versioning Registry D.4. Federated Learner System Patterns D.5. AI Mode Switcher D.6. Multi-Model Decision-Maker D.7. Homogeneous Redundancy Operation Infrastructure Patterns D.8. Continuous RAI Validator D.9. RAI Sandbox D.10. RAI Knowledge Base D.11. RAI Digital Twin D.12. Incentive Registry D.13. RAI Black Box D.14. Global-View Auditor Summary 7 Pattern-Oriented Reference Architecture for Responsible-AI-by-Design Architectural Principles for Designing AI Systems Pattern-Oriented Reference Architecture Supply Chain Layer System Layer Operation Infrastructure Layer Summary 8 Principle-Specific Techniques for Responsible AI Fairness T.1. Fairness Assessor T.2. Discrimination Mitigator Privacy T.3. Encrypted-Data-Based Trainer T.4. Secure Aggregator T.5. Random Noise Data Generator Explainability T.6. Local Explainer T.7. Global Explainer Summary Part III: Case Studies 9 Risk-Based AI Governance in Telstra Policy and Awareness Telstra’s Definition of AI Awareness Assessing Risk Dimensions of Risk Levels of Risk Operation of the Risk Council Learnings from Practice Identifying and Registering Use Cases Support from Technology Tools Governance over the Whole Lifecycle Scaling Up Future Work 10 Reejig: The World’s First Independently Audited Ethical Talent AI How Is AI Being Used in Talent? Aggregating Siloed Data Across Multiple Sources Providing Decision-Making Support at Scale Reducing Unconscious Bias What Does Bias in Talent AI Look Like? Data Bias Human Bias Regulating Talent AI Is a Global Issue US Legislation Being Introduced European Legislation Being Introduced Reejig’s Approach to Ethical Talent AI Debiasing Strategies How Ethical AI Evaluation Is Done: A Case Study in Reejig’s World-First Independently Audited Ethical Talent AI Overview The Independent Audit Approach A Summary of the Results Recognition and Impact Project Overview About the Reejig Algorithm The Objectives of the Project The Approach The Ethical AI Framework Used for the Audit Ethical Principles Ethical Validation Functional Validation The Benefits of Ethical Talent AI Building Stronger and More Diverse Teams by Removing Bias Maintaining Privacy and Security Demonstrating Leadership Against Competitors Reejig’s Outlook on the Future of Ethical Talent AI Reejig Has Led the Way in AI Ethics from Day One A New Independent Audit of Reejig Is Already Underway The Future of Workforce AI Will Unlock Zero Wasted Potential 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 Humans Data Process System Governance Conclusion Human Data Process System Governance Part IV: Looking to the Future 12 The Future of Responsible AI Regulation Education Standards Tools Public Awareness Final Remarks Part V: Appendix Index A B C D E F G H I K L M N O P Q R S T U V W X Y Z