"This Handbook is designed as a practical guide for developers of AI systems, especially from early-stage startups, to navigate data protection obligations and ethical considerations, in a clear and actionable manner. Rather than serving as a comprehensive legal or technical manual, the Handbook co ... mplements existing global and domestic resources on data protection and responsible AI. It offers a context-aware framework grounded in core legal and ethical principles, encouraging developers, product teams, and founders to interpret and apply these principles in ways best suited to their technology and user base. Drawing from existing frameworks, the Handbook offers recommendations across the lifecycle of an AI system. [...] The Handbook is divided into two main sections: Section I: Data Protection – which unpacks key concepts and compliance requirements under India’s data protection law, with a focus on their relevance to AI development; Section II: Responsible AI – which explores AI development through widely recognised responsible AI principles and provides a framework for their practical application. Each section concludes with a checklist of actionable takeaways intended to support developers in embedding privacy and ethical safeguards from the earliest stages of product design through to deployment. We have annexed a few case studies at the end, which demonstrate how developers adopt privacy and responsible AI principles in real-world applications." (How to read this handbook, page 10) more

"As AI technology is rapidly progressing in capability and being adopted more widely across society, it is more important than ever to understand the potential risks AI may pose and how AI can be developed and deployed safely. Introduction to AI Safety, Ethics, and Society offers a comprehensive and ... accessible guide to this topic. This book explores a range of ways in which societies could fail to harness AI safely in coming years, such as malicious use, accidental failures, erosion of safety standards due to competition between AI developers or nation-states, and potential loss of control over autonomous systems. Grounded in the latest technical advances, this book offers a timely perspective on the challenges involved in making current AI systems safer. Ensuring that AI systems are safe is not just a problem for researchers in machine learning – it is a societal challenge that cuts across traditional disciplinary boundaries. Integrating insights from safety engineering, economics, and other relevant fields, this book provides readers with fundamental concepts to understand and manage AI risks more effectively." (Publisher description) more