How Does Interpretable vs Explainable Machine Learning Work?

Imagine you have two friends who help you solve puzzles: one tells you exactly how they solved it step by step, and the other just says “it worked” without explaining anything.

Interpretable machine learning is like your first friend, they show you every step of their thinking so you can understand why a puzzle piece fits where it does. It's like when you use a simple recipe to bake cookies: you know exactly what each ingredient does.

Explainable machine learning, on the other hand, is like your second friend, they might not tell you how they solved the puzzle right away, but if you ask, they’ll explain it in a way that makes sense after the fact. It's like when you use a complicated recipe to bake cookies, and later someone helps you figure out why certain ingredients made them taste better.

Interpretable models are built with simple rules, think of them as a short list of instructions. You can see each rule clearly, so it’s easy to understand how the model makes decisions.

Explainable models use more complex rules but give you tools to look back and see why they made certain choices. It's like having a robot that solves puzzles really fast, you might not know how it thinks at first, but if you ask it, it’ll show you what it saw.