chapter one

1 Seeing inside the black box

 

This chapter covers

  • The ever-increasing gap between model usability and understanding in modern data science
  • How foundational works—from Bayes to Breiman—still power today’s algorithms
  • Connecting foundational ideas to interpretability and accountability in the age of automation
  • A conceptual stack that reveals the layered logic behind modeling decisions
  • Historical literacy as protection against brittle systems and hidden bias

Imagine you’re flying a small passenger plane through dense fog. The autopilot is engaged, quietly handling the controls as you casually monitor the instruments. Your family and closest friends are on board, chatting behind you, trusting you with their safety. The panel is calm, all indicators read green, and the flight is smooth. You sip your coffee, glance at the dashboard, and trust the system. But then, a sharp jolt. The plane lurches. Sensors fail. The autopilot disengages. Alarms chirp. You’re suddenly in control. Do you know what to do?

1.1 The illusion of understanding

1.2 Why foundation still matters

1.3 Why it matters more than ever

1.3.1 Interpretability and accountability

1.3.2 Diagnostic power

1.3.3 Model selection and design

1.3.4 Ethical and epistemological insight

1.3.5 Beyond automation

1.4 The hidden stack of modern intelligence

1.5 What you’ll need

1.6 How this book will teach you

1.7 Summary