Rahul Dixit – författare

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.

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.

This book discusses blind investigation and recovery of digital evidence left behind on digital devices, primarily for the purpose of tracing cybercrime sources and criminals. It presents an overview of the challenges of digital image forensics, with a specific focus on two of the most common forensic problems. The first part of the book addresses image source investigation, which involves mapping an image back to its camera source to facilitate investigating and tracing the source of a crime. The second part of the book focuses on image-forgery detection, primarily focusing on “copy-move forgery” in digital images, and presenting effective solutions to copy-move forgery detection with an emphasis on additional related challenges such as blur-invariance, similar genuine object identification, etc. The book concludes with future research directions, including counter forensics. With the necessary mathematical information in every chapter, the book serves as a useful reference resource for researchers and professionals alike. In addition, it can also be used as a supplementary text for upper-undergraduate and graduate-level courses on “Digital Image Processing”, “Information Security”, “Machine Learning”, “Computer Vision” and “Multimedia Security and Forensics”.