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AI and Ethics Handbook [Medsker] - mklabgr - 08-30-2026
Book:AI and Ethics Handbook
Editor: Larry Medsker
Publication date: August 15, 2026 (hardcover, according to Springer’s bibliographic record)
Publisher: Springer, Cham / Springer Nature Switzerland AG
Edition: 1st
Length: XV + 991 pages
ISBN: 978-3-032-12200-1 (hardcover); 978-3-032-12201-8 (eBook)
Fields: Artificial Intelligence, Engineering Ethics, Philosophy of Technology
Summary
AI and Ethics Handbook is a large interdisciplinary reference work examining the ethical, legal, regulatory, and social problems created by artificial intelligence. Edited by Larry Medsker, it is deliberately written to be accessible to both technical and non-technical readers. Its central concern is not merely whether AI systems can perform particular tasks, but how they should be designed, governed, deployed, and controlled so that their benefits do not produce unacceptable social harms. Particular attention is paid to both intentional and unintended misuse, and to the interaction between technology, law, institutions, and society.
The handbook begins with fundamental problems of responsible AI, including fairness and bias, privacy and data protection, misinformation and disinformation, transparency, accountability, safety, cybersecurity, trustworthy and explainable AI, human oversight, accessibility, generative-AI misuse, the future of work, education, data governance, data provenance and algorithmic decision-making. Rather than treating ethics as an abstract philosophical add-on, many chapters consider how ethical principles can be incorporated directly into the development and management of AI systems. One chapter, for example, explores “ethics-by-design”, while another develops the concept of organizational AI-ethics maturity as a way for institutions such as universities to assess whether responsible-AI principles are actually embedded in their practices.
The later sections broaden the discussion to the effects of AI across society and specific industries. Topics include regulation and compliance, elections, healthcare, social media, children and young people, autonomous vehicles and weapons, robotics, sustainability and AI's environmental impact, government, manufacturing, agriculture, digital twins, quantum computing, the Internet of Things, law, intellectual property, finance, marketing, entertainment, religion, and artificial general intelligence. The result is less a single sustained argument than a comprehensive map of the emerging AI-ethics landscape. Its major message is that responsible AI cannot be achieved through better algorithms alone: trustworthy AI requires technical safeguards together with governance, reliable data, human accountability, institutional procedures, regulation, and continuing public debate.
Key takeaways
- AI ethics is a systems problem. Bias, privacy, safety and accountability depend on data, algorithms, organizations, regulation and human decision-makers rather than on the model alone.
- Governance must accompany technological development. Standards, auditing, regulation, human oversight and institutional responsibility are recurring themes throughout the handbook.
- Data provenance and quality are ethical issues. Knowing where training data originated, whether it is valid, and how it was obtained is fundamental to trustworthy AI.
- Generative and increasingly autonomous AI intensify existing problems. Misinformation, accountability, intellectual property, employment and misuse become more difficult as systems gain greater capabilities.
- The book is primarily a reference work. At roughly 1,000 pages and covering dozens of areas, it is best used as a handbook for researchers, educators, policymakers and AI practitioners rather than read as a conventional introductory textbook.
Overall:AI and Ethics Handbook provides a broad, contemporary survey of responsible AI. Its particular strength is its interdisciplinary approach: it connects the technical questions surrounding AI with philosophy, law, public policy, organizational governance and concrete applications. It should be especially useful as a starting point for investigating a particular ethical issue—such as algorithmic bias, explainability, generative-AI governance, AI in education, or regulation—because Springer explicitly positions the handbook's extensive references as gateways to further research.
Springer – AI and Ethics Handbook