Published by Addison-Wesley Professional (February 1, 2024) © 2024

Qinghua Lu | Liming Zhu | Jon Whittle | Xiwei Xu | CSIRO
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    ISBN-13: 9780138073886

    Responsible AI: Best Practices for Creating Trustworthy AI Systems ,1st edition

    Language: English

    THE FIRST PRACTICAL GUIDE FOR OPERATIONALIZING RESPONSIBLE AI˜FROM MUL TI°LEVEL GOVERNANCE MECHANISMS TO CONCRETE DESIGN PATTERNS AND SOFTWARE ENGINEERING TECHNIQUES.

     

    AI is solving real-world challenges and transforming industries. Yet, there are serious concerns about its ability to behave and make decisions in a responsible way. Operationalizing responsible AI is about providing concrete guidelines to a wide range of decisionmakers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organizational, and team level; software engineering best practices; architecture styles and design patterns; system-level techniques connecting code with data and models; and trade-offs in design decisions. Responsible AI includes a set of practices that technologists (for example, technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure the AI systems they develop or adopt are trustworthy throughout the entire lifecycle and can be trusted by those who use them. The book offers guidelines and best practices not just for the AI part of a system, but also for the much larger software infrastructure that typically wraps around the AI.

     

    • First book of its kind to cover the topic of operationalizing responsible AI from the perspective of the entire software development life cycle.
    • Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system engineering techniques.
    • Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering.
    • Reduce the risks of AI adoption, accelerate AI adoption in responsible ways, and translate ethical principles into products, consultancy, and policy impact to support the AI industry.
    • Online repository of patterns, techniques, examples, and playbooks kept up-to-date by the authors.
    • Real world case studies to demonstrate responsible AI in practice.
    • Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this defi nitive guide.

        Preface.. . . . . . . . . . . . . . . . . xv

        About the Author.. . . . . . . . . . . . . . xix

    Part I Background and Introduction. . . . . . . . . . . . .1

    1 Introduction to Responsible AI. . . . . . . . . 3

        What Is Responsible AI?. . . . . . . . . . . . 4

        What Is AI?. . . . . . . . . . . . . . 6

        Developing AI Responsibly: Who Is Responsible for Putting the

        “Responsible” into AI?.. . . . . . . . . . . . 8

        About This Book.. . . . . . . . . . . . . 9

        How to Read This Book.. . . . . . . . . . . . 11

    2 Operationalizing Responsible AI: A Thought Experiment—Robbie the Robot.. . . . . . . . 13

        A Thought Experiment—Robbie the Robot.. . . . . . . . 13

        Summary. . . . . . . . . . . . . . 22

    Part II Responsible AI Pattern Catalogue. . . . . . . . . . .  23

    3 Overview of the Responsible AI Pattern Catalogue. . . . . 25

        The Key Concepts.. . . . . . . . . . . . . 25

        Why Is Responsible AI Different?. . . . . . . . . . 30

        A Pattern-Oriented Approach for Responsible AI.. . . . . . . 32

    4 Multi-Level Governance Patterns for Responsible AI.. . . . 39

        Industry-Level Governance Patterns. . . . . . . . . 42

        Organization-Level Governance Patterns.. . . . . . . . 56

        Team-Level Governance Patterns.. . . . . . . . . . 72

        Summary. . . . . . . . . . . . . . 85

    5 Process Patterns for Trustworthy Development Processes. . . 87

        Requirements.. . . . . . . . . . . . . 88

        Design. . . . . . . . . . . . . . . 96

        Implementation.. . . . . . . . . . . . . 105

        Testing. . . . . . . . . . . . . . . 110

        Operations. . . . . . . . . . . . . . 114

        Summary. . . . . . . . . . . . . . 120

    6 Product Patterns for Responsible-AI-by-Design.. . . . . 121

        Product Pattern Collection Overview.. . . . . . . . . 122

        Supply Chain Patterns. . . . . . . . . . . . 123

        System Patterns. . . . . . . . . . . . . 134

        Operation Infrastructure Patterns. . . . . . . . . 141

        Summary. . . . . . . . . . . . . . 158

    7 Pattern-Oriented Reference Architecture for Responsible-AI-by-Design. . . . . . . . . 159

        Architectural Principles for Designing AI Systems. . . . . . 160

        Pattern-Oriented Reference Architecture.. . . . . . . . 161

        Summary. . . . . . . . . . . . . . 165

    8 Principle-Specific Techniques for Responsible AI.. . . . . 167

        Fairness.. . . . . . . . . . . . . . 167

        Privacy. . . . . . . . . . . . . . . 172

        Explainability. . . . . . . . . . . . . 178

        Summary. . . . . . . . . . . . . . 182

    Part III Case Studies. . . . . . . . . . . . . . .  183

    9 Risk-Based AI Governance in Telstra. . . . . . . 185

        Policy and Awareness.. . . . . . . . . . . . 186

        Assessing Risk.. . . . . . . . . . . . . 188

        Learnings from Practice. . . . . . . . . . . 192

        Future Work. . . . . . . . . . . . . . 195

    10 Reejig: The World’s First Independently Audited Ethical Talent AI.. . . . . . . . . . . 197

        How Is AI Being Used in Talent?.. . . . . . . . . . 198

        What Does Bias in Talent AI Look Like?.. . . . . . . . 200

        Regulating Talent AI Is a Global Issue.. . . . . . . . . 201

        Reejig’s Approach to Ethical Talent AI. . . . . . . . . 202

        How Ethical AI Evaluation Is Done: A Case Study in Reejig’s World-First Independently Audited Ethical Talent AI. . . . . . . . 204

        Overview.. . . . . . . . . . . . . 204

        Project Overview. . . . . . . . . . . . . 206

        The Ethical AI Framework Used for the Audit.. . . . . . . 207

        The Benefits of Ethical Talent AI.. . . . . . . . . . 210

        Reejig’s Outlook on the Future of Ethical Talent AI.. . . . . . 211

    11 Diversity and Inclusion in Artificial Intelligence.. . . . . 213

        Importance of Diversity and Inclusion in AI.. . . . . . . 215

        Definition of Diversity and Inclusion in Artificial Intelligence. . . . 216

        Guidelines for Diversity and Inclusion in Artificial Intelligence. . . . 219

        Conclusion.. . . . . . . . . . . . . . 234

    Part IV Looking to the Future. . . . . . . . . . . . . 237

    12 The Future of Responsible AI.. . . . . . . . . 239

        Regulation. . . . . . . . . . . . . . 241

        Education.. . . . . . . . . . . . . . 242

        Standards.. . . . . . . . . . . . . . 244

        Tools.. . . . . . . . . . . . . . . 245

        Public Awareness.. . . . . . . . . . . . 246

        Final Remarks.. . . . . . . . . . . . . 246

    Part V Appendix. . . . . . . . . . . . . . . . 249

     

    9780138073923, TOC, 11/7/2023