"Ethics, Fairness, and Bias in Deep Learning with R" provides a comprehensive, practical, and original guide to understanding and addressing ethical challenges in deep learning applications. The book bridges theory and practice, exploring critical issues such as fairness, bias, transparency, accountability, privacy, and governance, while showing how to implement ethical AI workflows using R. Through detailed examples, case studies, and hands-on guidance with R packages like DALEX, fairmodels, and auditor, readers will learn to design, audit, and deploy deep learning models responsibly. This book is ideal for data scientists, AI practitioners, researchers, and policymakers who seek to integrate ethical principles into machine learning pipelines without sacrificing analytical rigor or technical excellence.
