Angol
Ready to go beyond the headlines and see how AI ethics actually works on the ground across the globe?
This book is your passport to the world of applied AI ethics. It leaves abstract theory behind. We explore the real, complex challenges facing nations today. The book uses a clear case study approach. Each chapter focuses on one country and one critical theme. You will travel to Germany to understand data privacy under the EU AI Act. Then, we dissect China's AI-powered Social Credit System. We'll investigate algorithmic bias in the United States criminal justice system. You'll see how the UK balances innovation and patient rights in its national healthcare AI strategy. We explore Japan's pioneering use of robotics to care for its aging population. Journey to France to untangle the debate over generative AI and copyright. See how Canada weighs the environmental costs of AI. Finally, we examine the crucial fight for Indigenous Data Sovereignty in Australia. This is a ground-level view of AI's biggest questions.
So, what makes this book different from others on AI ethics? Many books explain the what-the core principles of fairness, accountability, and transparency. This book explains the how and the why. We provide a truly global and comparative perspective that other books lack. Instead of just listing principles, we show them in action, clashing with cultural values, legal traditions, and national priorities. You'll understand why a rights-based European model differs so much from a state-driven approach or a market-focused one. This book provides a nuanced map of emerging global norms, offering a deeper, more practical understanding of the real-world trade-offs and solutions being forged today. It gives you the competitive advantage of seeing the full, complex picture of global AI governance.
Copyright Disclaimer: The author has no affiliation with any government or regulatory board mentioned herein. This work is independently produced, and references to organizations, policies, and frameworks are made under the principle of nominative fair use for commentary and analysis.
