Responsible AI

Bridge the gap between groundbreaking AI innovation and ethical responsibility with this comprehensive guide to the expert-led frameworks needed to navigate the complex legal, social, and moral landscapes of our digital future. Artificial Intelligence (AI) has emerged as a transformative force with the ability to bring new innovations to reshape economies, industries, and our daily lives. From advanced medical diagnostics to autonomous vehicles, AI systems are driving incomparable innovations in every sector. These advancements promise unmatched benefits and provide the potential to solve some of humanity’s most pressing challenges. However, there are many potential challenges and significant risks that come alongside the benefits provided by AI. This book offers a multidisciplinary viewpoint on how to develop and use AI systems responsibly by offering a deep dive into the ethical, legal, and societal ramifications of artificial intelligence. It explores important subjects such as algorithmic fairness, transparency, accountability, and governance through contributions from notable academics, engineers, and policy specialists. It highlights how crucial it is to match AI development with democratic norms and human values, offering both theoretical frameworks and workable implementation solutions for a range of industries. This comprehensive guide is an essential resource for scholars, professionals, and legislators dedicated to making sure that AI technology is created and applied in ways that are moral, inclusive, and advantageous to society. The reader will find the volume:

• Provides a multidisciplinary exploration of the ethical, legal, and social dimensions of AI;
• Bridges the gap between AI theory and real-world applications through practical frameworks;
• Covers key topics such as fairness, transparency, accountability, and governance;
• Serves as a valuable resource for researchers, practitioners, and policymakers aiming to build trustworthy AI systems.

Audience AI practitioners, data scientists, developers, business leaders, and executives actively engaged in the development and implementation of AI systems. Manish Kumar, PhD is an Assistant Professor at the Thapar Institute of Engineering and Technology, India, with more than eight years of teaching experience. He has authored several scientific articles in international journals and conferences, as well as internationally published books and book chapters. His research interests include soft computing applications for bioinformatics problems and computational intelligence. Nitigya Sambyal, PhD is an Assistant Professor in the Department of Computer Science and Engineering at the Thapar Institute of Engineering and Technology, India. She is also a postdoctoral fellow in the Department of Information Technology at Uppsala University, Sweden. Her research interests include machine learning, deep learning, medical image analysis, and computer vision. Leena Priyadarshini Singh, PhD is an Assistant Professor in Organizational Behavior and Industrial Relations with more than 14 years of experience. She has published more than 30 research papers in refereed international journals and several chapters in edited books. Her research interests include quality of work life, work-life balance, strategic leadership, corporate governance, and corporate social responsibility. Ramasamy V., PhD is an Associate Professor in the Dr. Sagunthala Research and Development Institute of Science and Technology, Vel Tech Rangarajan, India. He is the author of several scholarly research papers in national and international journals and conferences and editor of several books. His areas of interest include mobile cloud computing, IoT, data science, artificial intelligence, and data mining. S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting firm with more than 20 years of experience. He has published more than 100 books, 300 papers in international journals and conferences, and 300 patents. He specializes in technology forecasting and decision-making for leading companies and startups.