How to Use This Book

This book is designed to be flexible. Whether you're taking a 15-week college course, a 10-week accelerated program, or studying on your own, there's a path through this material that works for you.

Three Reading Paths

Each chapter includes guidance for three types of readers:

🏃 Fast Track — For readers with some background in AI or technology. Tells you which sections to skim and which exercises to complete to verify your understanding. Lets you move through the book at roughly twice the standard pace.

📖 Standard Path — The default. Read every chapter in order, complete the exercises and quizzes, and work on the progressive project. This is the path most undergraduate courses will follow.

🔬 Deep Dive — For motivated learners who want more. Points you to advanced case studies, extension exercises, and external resources. Adds roughly 50% more material to each chapter.

Chapter Structure

Every chapter follows a consistent structure:

  1. Opening quote and overview — What you'll learn and why it matters
  2. Main content (5–7 sections) — With retrieval practice prompts, worked examples, and content blocks
  3. Project Checkpoint — How to apply this chapter to your AI Audit Report
  4. Chapter Summary — Key concepts, debates, and frameworks
  5. Spaced Review — Questions revisiting material from earlier chapters
  6. What's Next — Preview of the next chapter

In separate files alongside each chapter: - Exercises — Multiple difficulty levels, from foundational to research-level - Quiz — Self-assessment with detailed answer explanations - Case Studies — Two in-depth case studies per chapter - Key Takeaways — One-page summary for quick reference - Further Reading — Annotated recommendations

Callout Blocks

Throughout the book, you'll encounter these callout blocks:

💡 Intuition: Mental models and analogies to build understanding

📊 Real-World Application: How this concept plays out in practice

⚠️ Common Pitfall: Mistakes to avoid, with explanations

🎓 Advanced: Graduate-level extensions — safe to skip on first reading

✅ Best Practice: Expert-recommended approaches

📝 Note: Additional context or nuance

🔗 Connection: Links to concepts in other chapters

🌍 Global Perspective: How this differs across cultures or regions

🔄 Check Your Understanding: Quick retrieval practice (try before checking the answer)

🧩 Productive Struggle: A problem to attempt before the technique is taught

🔍 Why Does This Work?: Deeper reasoning prompts

🪞 Learning Check-In: Metacognitive reflection

📐 Project Checkpoint: Progressive project increment

🚪 Threshold Concept: An idea that fundamentally transforms understanding

📜 Historical Context: How concepts evolved over time

The Progressive Project: AI Audit Report

Across all 21 chapters, you'll build an AI Audit Report on a real AI system that affects your life. You'll choose a system — a recommendation algorithm, a hiring tool, a content moderation system, a generative AI platform, etc. — and progressively analyze it:

  • What it does and how it likely works
  • What data it uses and where that data comes from
  • Who it affects and how
  • What biases it might have
  • How it's governed (or not)
  • What improvements you'd recommend

The final report is structured for a policy audience and is portfolio-ready. See Appendix D for templates and worksheets.

Optional Python Code

Some chapters include optional Python code examples marked clearly:

🐍 Optional Code: This code example is supplementary. You can understand the chapter fully without it.

If you want to run the code, see Appendix E for environment setup instructions.

Chapter Dependency Graph

Most chapters build on earlier ones, but not all chapters depend on every prior chapter. The graph below shows which chapters are prerequisites for which. Use it to customize your reading order or to identify which chapters you can skip.

graph TD
    Ch1["1. What Is AI?"] --> Ch2["2. History of AI"]
    Ch1 --> Ch3["3. How Machines Learn"]
    Ch3 --> Ch4["4. Data"]
    Ch3 --> Ch5["5. Large Language Models"]
    Ch3 --> Ch6["6. Computer Vision"]
    Ch4 --> Ch7["7. AI Decision-Making"]
    Ch5 --> Ch7
    Ch3 --> Ch8["8. When AI Gets It Wrong"]
    Ch5 --> Ch8
    Ch4 --> Ch9["9. Bias and Fairness"]
    Ch7 --> Ch9
    Ch1 --> Ch10["10. AI and Work"]
    Ch3 --> Ch10
    Ch5 --> Ch11["11. AI and Creativity"]
    Ch4 --> Ch12["12. Privacy & Surveillance"]
    Ch7 --> Ch12
    Ch9 --> Ch13["13. Governing AI"]
    Ch12 --> Ch13
    Ch5 --> Ch14["14. Using AI Effectively"]
    Ch8 --> Ch14
    Ch1 --> Ch15["15. AI in Healthcare"]
    Ch9 --> Ch15
    Ch1 --> Ch16["16. AI in Education"]
    Ch5 --> Ch17["17. AI and Justice"]
    Ch9 --> Ch17
    Ch3 --> Ch18["18. Environmental AI"]
    Ch10 --> Ch18
    Ch13 --> Ch19["19. Global Perspectives"]
    Ch8 --> Ch20["20. AI Safety"]
    Ch13 --> Ch20
    Ch1 --> Ch21["21. The Road Ahead"]

Reading the graph: An arrow from Chapter A to Chapter B means you should read A before B. If no arrow connects two chapters, they can be read in either order.

For Instructors

The instructor-guide/ directory contains: - Three syllabi: 15-week, 10-week, and self-paced - Chapter-by-chapter teaching notes with lecture sequences, discussion prompts, and common student struggles - Discussion guides for each chapter - Additional assessments: midterm, final exam, and rubrics - Common struggles guide with intervention strategies

Getting Help

If you're stuck on a concept, try these strategies: 1. Re-read the relevant 💡 Intuition block 2. Complete the 🔄 Check Your Understanding prompts 3. Work through the exercises starting from Part A (foundational level) 4. Check the Key Takeaways summary 5. Review prerequisite chapters listed in the chapter header

Remember: struggling with new ideas is normal and productive. If a concept doesn't click immediately, keep going — the spaced review system will bring it back, and it often makes more sense the second time.