Understanding the Ethics of Generative AI

A Developer's Guide
1. Auflage 2026
ISBN: 978-1-041-15318-4
Verlag: Taylor & Francis Ltd

Buch, Englisch, 180 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 435 g

The book discusses the ethical complexities that software developers face as they build AI systems capable of autonomous creation. It explores the ethical decisions developers must make when building generative AI systems, from mitigating bias in training data to protecting user privacy and navigating regulatory compliance. Through real-world case studies and actionable frameworks, it equips technical professionals with both the understanding and tools to build AI systems that are fair, transparent, and worthy of public trust.

This book covers the following topics:

- Identifies and corrects algorithmic bias in training datasets, ensuring AI systems produce equitable outputs that don’t systematize discrimination.

- Designs privacy-first AI architectures and implements transparency practices that comply with data protection regulations while building user trust.

- Navigates evolving legal and regulatory landscapes (GDPR, AI Act, sector-specific rules), helping teams stay ahead of compliance requirements.

- Applies ethical frameworks to real-world decisions: what to do when fairness and accuracy conflict, how to audit AI systems for hidden harms, when to say no to a project.

- Provides a governance model for embedding ethics into development workflows, not as an afterthought but as a core design practice.

Zielgruppe

Academic

Autoren/Hrsg.

Gaur, Loveleen

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Geisteswissenschaften Philosophie Angewandte Ethik & Soziale Verantwortung Wissenschaftsethik, Technikethik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Geisteswissenschaften Philosophie Rechtsphilosophie, Rechtsethik

Inhaltsverzeichnis

Chapter 1: Demystifying Generative AI. Chapter 2: Comprehending Training Data. Chapter 3: Strategies for Mitigating Bias in AI Development. Chapter 4: Techniques for Ethical Calibration of AI Models. Chapter 5: Addressing Privacy Challenges in Generative AI. Chapter 6: Ensuring Transparency and Accountability in AI Systems. Chapter 7: Ethical Considerations of Autonomous AI Decision-Making. Chapter 8: Navigating Regulation and Governance in Generative AI. Chapter 9: Developers’ Responsibilities in Promoting Ethical AI. Chapter 10: Future Directions: Toward Sustainable and Ethical AI Development.

Über Autor(innen)

Dr Loveleen Gaur is a senior academic, researcher, and international examiner with over two decades of experience in higher education, doctoral supervision, and research evaluation. She holds a PhD in Computer Applications and specializes in Artificial Intelligence, Generative AI, Data Science, and Business Analytics, with strong interdisciplinary applications across management, healthcare, and digital systems.

She currently serves as Faculty Lead of the Doctoral Program in Deep Technologies at Universidad Nacional Mayor de San Marcos, Lima, Peru, and as Dissertation Committee Chair of the DBA Program at Golden Gate University Worldwide, San Francisco, USA. She is also Research Professor at Alliance University, Bengaluru, India, and Adjunct Professor at the University of the South Pacific, Fiji. In addition, she serves as an Assessor for Innovate UK (UK Research and Innovation) and as a Judge for the QS Reimagine Education Awards. Earlier, as Professor at Amity University, India, she founded its postgraduate programs in AI and Business Analytics.

Dr Gaur has served as an External PhD Examiner and doctoral committee member for several international universities, including Taylor’s University, Malaysia; Auckland University of Technology, New Zealand; Alliance University, India; and the Symbiosis Centre for Research and Innovation.

She has an extensive publication record, with more than 150 research outputs and over 5,000 citations in high-impact journals indexed in Scopus and Web of Science, and has authored and edited multiple scholarly books with leading publishers such as Elsevier, Springer, Taylor & Francis, Wiley, and IGI Global. She serves as Co-Editor-in-Chief of Communications in Statistics: Case Studies, Data Analysis and Applications (Taylor & Francis) and as Series Editor of Concise Introductions to AI and Data Science (Wiley-Scrivener), along with several editorial and reviewer roles across reputed international journals.

Recognized globally for her research impact, Dr Gaur is listed among the Elsevier and Stanford University World’s Top 2% Scientists (2024 and 2025) and is a Senior Member of IEEE.