Intro
Table of Contents
About the Author
About the Technical Reviewer
Introduction
Part I: Foundation
Chapter 1: Responsibility
Avoiding the Blame Game
Being Accountable
Eliminating Toxicity
Thinking Fairly
Protecting Human Privacy
Ensuring Safety
Summary
Chapter 2: AI Principles
Fairness, Bias, and Human-Centered Values
Google
The Organisation for Economic Cooperation and Development (OECD)
The Australian Government
Transparency and Trust
Accountability
Social Benefits
Privacy, Safety, and Security
Summary

Chapter 3: Data
The History of Data
Data Ethics
Ownership
Data Control
Transparency
Accountability
Equality
Privacy
Intention
Outcomes
Data Curation
Best Practices
Annotation and Filtering
Rater Diversity
Synthetic Data
Data Cards and Datasheets
Model Cards
Tools
Alternative Datasets
Summary
Part II: Implementation
Chapter 4: Fairness
Defining Fairness
Equalized Odds
Equal Opportunity
Demographic Parity
Fairness Through Awareness
Fairness Through Unawareness
Treatment Equality
Test Fairness

Counterfactual Fairness
Fairness in Relational Domains
Conditional Statistical Parity
Types of Bias
Historical Bias
Representation Bias
Measurement Bias
Aggregation Bias
Evaluation Bias
Deployment Bias
Measuring Fairness
Fairness Tools
Summary
Chapter 5: Safety
AI Safety
Autonomous Learning with Benign Intent
Human Controlled with Benign Intent
Human Controlled with Malicious Intent
AI Harms
Discrimination, Hate Speech, and Exclusion
Information Hazards
Misinformation Harms
Malicious Uses
Human-Computer Interaction Harms

Environmental and Socioeconomic Harms
Mitigations and Technical Considerations
Benchmarking
Summary
Chapter 6: Human-in-the-Loop
Understanding Human-in-the-Loop
Human Annotation Case Study: Jigsaw Toxicity Classification
Rater Diversity Case Study: Jigsaw Toxicity Classification
Task Design
Measures
Results and Conclusion
Risks and Challenges
Summary
Chapter 7: Explainability
Explainable AI (XAI)
Implementing Explainable AI
Data Cards
Model Cards
Open-Source Toolkits
Accountability
Dimensions of AI Accountability
Governance Structures

Data
Performance Goals and Metrics
Monitoring Plans
Explainable AI Tools
Summary
Chapter 8: Privacy
Privacy Preserving AI
Federated Learning
Digging Deeper
Differential Privacy
Differential Privacy and Fairness Tradeoffs
Summary
Chapter 9: Robustness
Robust ML Models
Sampling
Bias Mitigation (Preprocessing)
Data Balancing
Data Augmentation
Cross-Validation
Ensembles
Bias Mitigation (In-Processing and Post-Processing)
Transfer Learning
Adversarial Training
Making Your ML Models Robust
Establish a Strong Baseline Model