Dilip K. Prasad

This unique compendium explores the crucial role of inclusivity in the development, deployment, and governance of artificial intelligence systems. It addresses the growing ethical and social concerns surrounding the lack of representation in AI, emphasizing how gender and diversity are integral to creating fair, responsible, and innovative technologies. The book delves into the ethical, social, and economic benefits of promoting diversity in AI teams and research. Covering a wide range of topics, from data biases to global policy frameworks, and through case studies and real-world examples, it illustrates both the successes and challenges in achieving gender and diversity in the field. This useful reference text is designed for academics, AI professionals, policymakers, and corporate leaders of machine learning and AI, and innovation and technology.

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.