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

Published by Addison-Wesley Professional (December 19, 2023) © 2024

  • Qinghua Lu
  • Liming Zhu
  • Jon Whittle
  • Xiwei Xu

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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

    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

Dr. Qinghua Lu is a principal research scientist and leads the Responsible AI science team at CSIRO’s Data61. She received her PhD from University of New South Wales in 2013. Her current research interests include responsible AI, software engineering for AI/GAI, and software architecture. She has published 150+ papers in premier international journals and conferences. Her recent paper titled “Towards a Roadmap on Software Engineering for Responsible AI” received the ACM Distinguished Paper Award. Dr. Lu is part of the OECD.AI’s trustworthy AI metrics project team. She also serves a member of Australia’s National AI Centre Responsible AI at Scale think tank. She is the winner of the 2023 APAC Women in AI Trailblazer Award.

Dr./Prof. Liming Zhu is a Research Director at CSIRO’s Data61 and a conjoint full professor at the University of New South Wales (UNSW). He is the chairperson of Standards Australia’s blockchain committee and contributes to the AI trustworthiness committee. He is a member of the OECD.AI expert group on AI Risks and Accountability, as well as a member of the Responsible AI at Scale think tank at Australia’s National AI Centre. His research program innovates in the areas of AI/ML systems, responsible/ethical AI, software engineering, blockchain, regulation technology, quantum software, privacy, and cybersecurity. He has published more than 300 papers on software architecture, blockchain, governance and responsible AI. He delivered the keynote “Software Engineering as the Linchpin of Responsible AI” at the International Conference on Software Engineering (ICSE) 2023.

Prof. Jon Whittle is Director at CSIRO’s Data61, Australia’s national centre for R&D in data science and digital technologies. With around 850 staff and affiliates, Data61 is one of the largest collections of R&D expertise in Artificial Intelligence and Data Science in the world. Data61 partners with more than 200 industry and government organisations, more than 30 universities, and works across vertical sectors in manufacturing, health, agriculture, and the environment. Prior to joining Data61, Jon was Dean of the Faculty of Information Technology at Monash University.

Dr. Xiwei Xu is a principal research scientist and the group leader of the software systems research group at Data61, CSIRO. With a specialization in software architecture and system design, she is  at the forefront of research in these fields. Xiwei is identified by the Bibliometric Assessment of Software Engineering Scholars and Institutions as a top scholar and ranked 4th in the world (2013–2020) as the most impactful SE researchers by JSS (Journal of Systems and Software), a well-recognized academic journal in software engineering research.

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