Prerequisites

What You Need to Know Before Reading This Book

This textbook is designed to be accessible to readers without technical backgrounds in computer science, mathematics, or statistics. It is written for business professionals, policy analysts, lawyers, managers, and students in non-technical graduate programs. Here is what you do and don't need before beginning.

What You Need

1. Basic Familiarity with What AI Is

You don't need to know how machine learning works mathematically, but it helps to have a general sense of what we mean when we say an AI system "learned" to do something. Specifically:

  • AI systems, particularly those based on machine learning, identify patterns in large datasets and use those patterns to make predictions or decisions
  • They are not explicitly programmed with rules; they derive rules from data
  • Their outputs — recommendations, classifications, scores, decisions — reflect patterns in whatever data they were trained on

If this is unfamiliar, Chapter 1 provides enough grounding to proceed, and the FAQ answers common technical questions without requiring a computer science background.

2. Some Professional or Organizational Context

This textbook is most useful if you can connect what you read to an organizational context — your employer, your field, your sector. You don't need to work with AI directly; you may use products or services that incorporate AI, operate under regulations that govern AI, or manage people who work on AI systems. Any of these contexts will make the material more concrete.

3. Willingness to Sit with Ambiguity

Many of the questions in AI ethics don't have clean answers. The most important prerequisite for this book is comfort with trade-offs, competing values, and questions that resist simple resolution. If you are looking for a rulebook that tells you exactly what to do, you will be disappointed — and you may be suspicious of any book that claims to provide one.

What You Don't Need

No Computer Science Required

You do not need to know how to code, how neural networks are structured, or how gradient descent works. The chapters that include Python code explain what the code does in plain English, and you can skip the code blocks entirely without losing the conceptual thread.

No Philosophy Required

No background in academic philosophy is assumed. When philosophical concepts are introduced — consequentialism, deontology, virtue ethics, the capabilities approach — they are explained from scratch. The Primary Source Anthology provides brief extracts from key philosophical texts for readers who want to go deeper.

No Statistics Required

Fairness metrics and bias measurement are explained conceptually before any mathematical notation is introduced. The few quantitative concepts that appear (confusion matrices, rates, proportions) are explained with examples before formulas. A Python Reference Appendix supports readers who want to implement fairness calculations but does not require statistical training.

Regulatory frameworks including GDPR, the EU AI Act, and U.S. anti-discrimination law are explained at a conceptual level accessible to non-lawyers. Chapter 20 (Liability Frameworks) and Chapter 33 (Regulation and Compliance) are written for business professionals, not attorneys.

Helpful Background (Not Required)

The following backgrounds will enrich your reading but are not necessary:

  • Business strategy or management: Makes Part 4 (Accountability) and Part 6 (Governance) especially relevant
  • Human resources: Makes Chapter 10 (Bias in Hiring) immediately practical
  • Healthcare administration: Enriches Chapters 12 and 36
  • Government or policy: Makes Parts 4 and 6 especially relevant
  • Journalism or communications: Enriches Chapters 15, 16, and 29

Self-Assessment Questions

Before starting, consider where you stand on these questions. Return to them after completing the book:

  1. Can you name three ways that an AI system could cause harm without being "broken"?
  2. What ethical framework do you implicitly use when making difficult professional decisions?
  3. Who in your organization is responsible if an AI system your company uses discriminates against a customer?
  4. What data does your organization collect, and who has rights over it?
  5. What is one thing about AI that you find genuinely uncertain or troubling?

Glossary and Reference

If you encounter unfamiliar terms, the Glossary covers all key terms introduced throughout the book. Terms are defined at first use within each chapter, and a Vocabulary Builder box highlights especially important concepts.