Explainability in deep learning is like the secret menu of a fancy restaurant. You know, the one with the food that tastes like cardboard, but explainability is where the magic happens.
Why Do We Need It? for more information on the importance of explainability in deep learning models.Why Do We Need It?
Because humans are curious, and also because deep learning models are like magic boxes that we think we understand but really don't. We need explainability to figure out how they work, and why they're making us buy things we don't need.
How Is It Done? to learn more about the intricacies of explainability.How Is It Done?
We use feature attribution, SHAP values, and Layer-wise Relevance to figure out how the model is deciding things.
What Is Feature Attribution? for more information on the intricacies of feature attribution.What Is Feature Attribution?
It's like giving credit where credit is due. We want to know which features of the input data are driving the model's decisions.