Explainable AI (XAI) has developed as a subfield of Artificial Inteligence, focussing on exposing complex AI models to humans in a systematic and interpretable manner. This area explores, discusses the steps and models involved in making intelligent decisions. This series will cover the working behavior and explains the ability of powerful algorithms such as neural networks, ensemble methods including random forests, and other similar algorithms to sacrifice transparency and explainability for power, performance, and accuracy in different engineering applications relates to the real world. Aimed at graduate students, academic researchers and professionals, the proposed series will focus key topics including XAI techniques for engineering applications, Explainable AI for Deep Neural Network Predictions, Explainable AI for Machine learning Predictions, XAI driven recommendation systems for Automobile and Manufacturing Industries, and Explainable AI for Autonomous Vehicles.
Edited By Vikas Chaudhary, Moolchand Sharma, Prerna Sharma, Deevyankar Agarwal
December 16, 2021
Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a ...