The first practical guide for operationalizing responsible AI-from multi-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 guides to a wide range of decision-makers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organization and team level, software engineering best practices, architecture styles and design patterns, system-level techniques connecting code with data and model, and the trade-offs in decisions.
Responsible AI includes a set of practices that technologists (e.g., technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure that the developed AI systems are trustworthy throughout the entire lifecycle and trusted by those who use and rely on them. The book offers guidelines and best practices not just for the AI, which is typically a small part of a larger system, but also for the systems engineering process and organizational governance.
First book of its kind about operationalizing responsible AI from the perspective of the entire software development life cycle, complete with real world case studies.
Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system techniques most used by software companies.
Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering.
Reduce the risk 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.
Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this definitive guide.
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author(s): Dr. Qinghua Lu; Dr./Prof. Liming Zhu; Prof. Jon Whittle; Dr. Xiwei Xu
Publisher: Addison-Wesley
Year: 2023
Language: English
Commentary: raw & unedited
Pages: 702
Cover Page
Halftitle Page
Title Page
Copyright Page
Pearson’s Commitment to Diversity, Equity, and Inclusion
Dedication Page
Contents
Table of Contents
Preface
Acknowledgments
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
Summary
Part II: Responsible AI Pattern Catalogue
3. Overview of the Responsible AI Pattern Catalogue
The Key Concepts
Why Is Responsible AI Different?
A Pattern-Oriented Approach for Responsible AI
4. Multi-Level Governance Patterns for Responsible AI
Industry-Level Governance Patterns
Organization-Level Governance Patterns
Team-Level Governance Patterns
Summary
5. Process Patterns for Trustworthy Development Processes
Requirements
Design
Implementation
Testing
Operations
Summary
6. Product Patterns for Responsible-AI-by-Design
Product Pattern Collection Overview
Supply Chain Patterns
System Patterns
Operation Infrastructure Patterns
Summary
7. Pattern-Oriented Reference Architecture for Responsible-AI-by-Design
Architectural Principles for Designing AI Systems
Pattern-Oriented Reference Architecture
Summary
8. Principle-Specific Techniques for Responsible AI
Fairness
Privacy
Explainability
Summary
Part III: Case Studies
9. Risk-Based AI Governance in Telstra
Policy and Awareness
Assessing Risk
Learnings from Practice
Future Work
10. Reejig: The World’s First Independently Audited Ethical Talent AI
How Is AI Being Used in Talent?
What Does Bias in Talent AI Look Like?
Regulating Talent AI Is a Global Issue
Reejig’s Approach to Ethical Talent AI
How Ethical AI Evaluation Is Done: A Case Study in Reejig’s World-First Independently Audited Ethical Talent AI
Project Overview
The Ethical AI Framework Used for the Audit
The Benefits of Ethical Talent AI
Reejig’s Outlook on the Future of Ethical Talent AI
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
Conclusion
Part IV: Looking to the Future
12. The Future of Responsible AI
Regulation
Education
Standards
Tools
Public Awareness
Final Remarks
Part V: Appendix
Governance Patterns
Process Patterns
Product Patterns
Principle-Specific Techniques
