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This book aims to unravel the complex tapestry that interweaves strategic decision-making models with the forefront of deep learning techniques. Applications of Game Theory in Deep Learning provides an extensive and insightful exploration of game theory in deep learning, diving deep into both the theoretical foundations and the real-world applications that showcase this intriguing intersection of fields. Starting with the essential foundations for comprehending both game theory and deep learning, delving into the individual significance of each field, the book culminates in a nuanced examination of Game Theory's pivotal role in augmenting and shaping the development of Deep Learning algorithms. By elucidating the theoretical underpinnings and practical applications of this synergistic relationship, we equip the reader with a comprehensive understanding of their combined potential. In our digital age, where algorithms and autonomous agents are becoming more common, the combination of game theory and deep learning has opened a new frontier of exploration. The combination of these two disciplines opens new and exciting avenues. We observe how artificial agents can think strategically, adapt to ever-shifting environments, and make decisions that are consistent with their goals and the dynamics of their surroundings. This book presents case studies, methodologies, and real-world applications.
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This book addresses critical ethical, social, and technological challenges that arise from the rapid integration of Generative AI systems into diverse aspects of human life. The shift toward human-centered GAI emerges as a response to increasing concerns about AI's embedded biases, opacity, and potential misalignment with human values. By centering on human experiences, agency, and ethical principles, this paradigm fosters the development of GAI that is explainable, interpretable, trustworthy, and aligned with societal needs.Building Human-Centered Generative AI offers a comprehensive roadmap for rethinking generative AI as a technology that serves people first, not metrics or scale. Bringing together principles from computer science, human-computer interaction, ethics, psychology, and design, it shows how empathy, fairness, transparency, and accountability can be systematically embedded into models, interfaces, and deployment lifecycles. Rather than treating ethics as an afterthought, the book positions human values, user needs, and social impact as core design requirements for any serious generative AI initiative.Written for graduate students, researchers, practitioners, and policymakers, this book functions both as a scholarly reference and as a practical playbook for responsible innovation. It equips teams with actionable methods for participatory and value-sensitive design, human-in-the-loop collaboration, GenAIOps governance, and multidimensional evaluation focused on trust, usability, and societal impact. For anyone tasked with designing, deploying, or regulating generative AI, Building Human-Centered Generative AI provides the conceptual grounding and hands-on guidance needed to align powerful new technologies with human dignity, equity, and long-term social good.