Interpretability (machine learning)

Interpretability in machine learning means being able to understand how a computer program made a decision. It's like asking a friend why they picked a certain toy or game to play with - you want to know their reasons. In the same way, people want to know why a computer program made a certain decision.

Imagine you have a robot friend who only speaks a different language than you, but it can tell you which animal is in a picture. You show it a picture of a cat and ask it "what is this?" The robot responds with "meow." You'd think that's kind of funny, but you still don't understand why it said that.

To make the robot's answer understandable and useful, you need interpretability. This means knowing how the robot got to its answer. In machine learning, interpretability means knowing what data was used to train the computer program and what rules it followed to make decisions. For example, did the program only look at the cat’s whiskers and ears to guess it was a cat, or did it also look at its fur and tail?

Having interpretability is important for two big reasons. The first is that it helps us trust the machine learning model. If we know how it’s making decisions, we can evaluate if it's working well or not. The second is that interpretability can help us find and fix problems with the model. For example, if we notice the model only looks at one feature of a picture, we can change the rules or add more data to make it a better model.

In summary, interpretability in machine learning is like asking your robot friend why it did something. It’s important because we can trust the machine learning model and make it better.