By Jayin Khanna | Topic Report
Abstract
As deep learning models become more complex and are deployed in high-stakes domains like healthcare and finance, understanding why a model makes a particular decision is crucial. Explainable AI (XAI) provides techniques to interpret and understand the inner workings of these black box models. This report focuses on attribution techniques, which aim to assign importance scores to input features based on their contribution to the model's output.