Responsible AI

Name: Responsible AI | Best Practices for Creating Trustworthy AI Systems
Brand: Addison Wesley
Price: 37.99 EUR
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Best Practices for Creating Trustworthy AI Systems

CSIRO Qinghua Lu Liming Zhu Jon Whittle Xiwei Xu(Autor*in)

Addison Wesley (Verlag)

1. Auflage

Erschienen am 1. Februar 2024

320 Seiten

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Beschreibung

THE FIRST PRACTICAL GUIDE FOR OPERATIONALIZING RESPONSIBLE AI?FROM MUL TI degreesLEVEL 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.

Weitere Details

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Personen

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.

Inhalt

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

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