Should firms that apply machine learning algorithms in their decision making make their algorithms transparent? Despite increasing calls for algorithmic transparency, most firms have kept their algorithms opaque citing potential gaming by users that may negatively affect the algorithm's...

An important concern is that algorithms can inadvertently discriminate against minority groups and reinforce existing inequality. Typically, the worry is that when classification algorithms are trained on a dataset that itself reflects bias this may reinforce bias. However, in the world of...

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there...

"This book presents content coverage on the basics of artificial intelligence, search problems and soft computing approaches, providing insight on robotics and basic algorithms, special topics in AI, planning, applied soft computing, applied AI, applied evolutionary computing"--Provided by...