Responsible AI
$44.99
| Title | Range | Discount |
|---|---|---|
| Trade Discount | 5 + | 25% |
- Description
- Additional information
Description
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.
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
Additional information
| Dimensions | 0.59 × 7.38 × 9.00 in |
|---|---|
| Imprint | |
| Format | |
| ISBN-13 | |
| ISBN-10 | |
| Author | |
| Subjects | professional, higher education, Employability, IT Professional, Y-AB SOFTWARE METHODS |
