Artificial Intelligence Ethics

Navigating Ethical Boundaries in AI

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This book deeply analyzes the theoretical roots of the development of global artificial intelligence ethics and AI governance, the ethical issues in AI application scenarios, and the discussion of artificial intelligence governance issues from a global perspective. From the perspective of knowledge, the book includes not only the metaphysical research of traditional Western ethics, but also the interpretation of AI-related practical cases and international policies. The purpose of this book is not only to study AI ethics and governance issues academically, but to seek a path to solve problems in the real world. It is a very meaningful monograph in both academic theory and reality. This book responds to the implementation of China's digital economy governance and other topics. It is a cutting-edge academic monograph that combines industry, policy, and thought.

This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments – the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in software testing/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.

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