Explainability for Deep Learning Models

Abstract

Deep learning models are becoming the backbone of artificial intelligence implementations. At the same time, it is super important to build the explainability layers to explain the predictions and output of the deep learning model. To build trust for the deep learning model outcome, we need to explain the results or output. At a high level, a deep learning layer involves more than one hidden layer, whereas a neural network layer has three layers: the input layer, the hidden layer, and the output layer. There are different variants of neural network models such as single hidden layer neural network model, multiple hidden layer neural networks, feedforward neural networks, and backpropagation neural networks. Depending upon the structure of the neural network model, there are three popular structures: recurrent neural networks, which are mostly used for sequential information processing, such as audio processing, text classification, etc.; deep neural networks, which are used for building extremely deep networks; and finally, convolutional neural network models, which are used for image classification.