AI Ethics: Bias, Fairness, Accountability, Transparency, Societal Impact, Governance, Privacy, and Security

Complete Table of Contents

Target: 1,200–1,600 pages | ~40 chapters | 8,000–12,000 words per chapter Audience: Business professionals, managers, executives, policy analysts Level: Comprehensive graduate/professional reference

Front Matter

Part 1: Foundations

Chapters 1–6 establish the conceptual, historical, philosophical, and institutional foundations of AI ethics. Readers who are new to the field should begin here; experienced practitioners will find these chapters useful for grounding their intuitions in rigorous frameworks.

Part 1 Introduction

Part 2: Bias and Fairness

Chapters 7–12 provide a comprehensive treatment of algorithmic bias — its sources, measurement, and manifestation across high-stakes domains including employment, finance, and healthcare.

Part 2 Introduction

Chapter 7: Understanding Algorithmic Bias

ch07-understanding-algorithmic-bias/ - Chapter Content - Exercises - Quiz - Case Study 1: Amazon's Hiring Algorithm and Gender Bias - Case Study 2: Racial Bias in Facial Recognition — NIST Findings - Key Takeaways - Further Reading

Chapter 8: Sources of Bias in Data and Models

ch08-sources-of-bias/ - Chapter Content - Exercises - Quiz - Case Study 1: The Pulse Oximeter Problem — Medical Device Bias - Case Study 2: GPT-3 and the Encoding of Cultural Bias - Key Takeaways - Further Reading

Chapter 9: Measuring Fairness: Metrics and Trade-offs

ch09-measuring-fairness/ - Chapter Content - Exercises - Quiz - Case Study 1: COMPAS and the Impossibility of Fairness - Case Study 2: Fairness Metrics in Loan Approval Systems - Key Takeaways - Further Reading - Code: Fairness Metrics in Python

Chapter 10: Bias in Hiring and HR Systems

ch10-bias-hiring-hr/ - Chapter Content - Exercises - Quiz - Case Study 1: HireVue and AI-Powered Video Interviews - Case Study 2: Résumé Screening at Scale — Opportunity and Risk - Key Takeaways - Further Reading

Chapter 11: Bias in Financial Services and Credit

ch11-bias-financial-services/ - Chapter Content - Exercises - Quiz - Case Study 1: The Apple Card Gender Discrimination Controversy - Case Study 2: Algorithmic Redlining in Digital Lending - Key Takeaways - Further Reading - Code: Credit Scoring Bias Detection

Chapter 12: Bias in Healthcare AI

ch12-bias-healthcare-ai/ - Chapter Content - Exercises - Quiz - Case Study 1: Optum's Healthcare Algorithm and Racial Bias - Case Study 2: Skin Lesion Classifiers and Demographic Gaps - Key Takeaways - Further Reading

Part 3: Transparency and Explainability

Chapters 13–17 examine the imperative for AI transparency — why black-box systems present risks, what XAI techniques offer, how to communicate AI decisions to diverse stakeholders, and the emerging legal right to explanation.

Part 3 Introduction

Chapter 13: The Black Box Problem

ch13-black-box-problem/

Chapter 14: Explainable AI (XAI) Techniques

ch14-explainable-ai-techniques/

Chapter 15: Communicating AI Decisions to Stakeholders

ch15-communicating-ai-decisions/

Chapter 16: Transparency in AI Marketing and Advertising

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Chapter 17: The Right to Explanation

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Part 4: Accountability and Responsibility

Chapters 18–22 address who bears responsibility when AI systems cause harm, how auditing works in practice, emerging liability law, corporate governance structures, and the important role of internal dissent.

Part 4 Introduction

Chapter 18: Who Is Responsible When AI Fails?

ch18-who-is-responsible/

Chapter 19: Auditing AI Systems

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Chapter 20: Liability Frameworks for AI

ch20-liability-frameworks/

Chapter 21: Corporate Governance of AI

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Chapter 22: Whistleblowing and Ethical Dissent in AI Organizations

ch22-whistleblowing-ethical-dissent/

Part 5: Privacy and Security

Chapters 23–27 examine the intersection of AI with privacy rights and cybersecurity — from foundational data privacy principles through surveillance capitalism, biometric risks, and privacy-preserving technical approaches.

Part 5 Introduction

Chapter 23: Data Privacy Fundamentals

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Chapter 24: Surveillance Capitalism and AI

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Chapter 25: Cybersecurity and AI Systems

ch25-cybersecurity-ai/

Chapter 26: Biometrics and Facial Recognition Ethics

ch26-biometrics-facial-recognition/

Chapter 27: Privacy-Preserving AI Techniques

ch27-privacy-preserving-ai/

Part 6: Societal Impact and Governance

Chapters 28–34 examine AI's effects at the societal level — on employment, democratic institutions, criminal justice, the environment, and the emerging landscape of global AI regulation.

Part 6 Introduction

Chapter 28: AI and Employment: Disruption and Opportunity

ch28-ai-employment/

Chapter 29: AI and Democratic Processes

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Chapter 30: AI in Criminal Justice Systems

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Chapter 31: The Environmental Cost of AI

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Chapter 32: Global AI Governance Frameworks

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ch33-regulation-compliance/

Chapter 34: AI Ethics in Emerging Markets

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Part 7: Emerging Issues and Special Topics

Chapters 35–39 examine the frontier — generative AI, autonomous systems, military applications, questions of machine consciousness, and anticipating the ethical challenges of coming decades.

Part 7 Introduction

Chapter 35: Generative AI Ethics

ch35-generative-ai-ethics/

Chapter 36: AI in Healthcare Decision-Making

ch36-ai-healthcare-decisions/

Chapter 37: Autonomous Weapons and Military AI

ch37-autonomous-weapons/

Chapter 38: AI Consciousness, Rights, and Moral Status

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Chapter 39: The Future of AI Ethics: Anticipating Tomorrow's Challenges

ch39-future-ai-ethics/

Part 8: Capstone Projects

Appendices

Core Appendices (All Classifications)

Research Methods (Category B)

Philosophical Sources (Category D)

Practical Skills (Category C)

Technical Reference (Python)

Python Reference for AI Fairness Auditing

Total estimated pages: 1,200–1,600 | Total chapters: 39 + Capstone | Appendices: 14