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.
- 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 core design practice
Table of Contents:
Chapter 1: The Dawn of Generative AI. Chapter 2: The Foundation of AI Understanding Training Data. Chapter 3: Strategies for Mitigating Biases in AI Development. Chapter 4: Technique for Ethical Calibration of Generative AI Models. Chapter 5: Addressing Privacy Challanges in Generative AI. Chapter 6: Ensuring Transparency and Accountability in AI Systems Chapter 7: Ethical Implications 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 - Towards Sustainable and Ethical AI Development.
About the Author :
Dr. Loveleen Gaur
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 Adjunct Professor at the University of the South Pacific (Fiji) and Visiting Faculty at Symbiosis International (Deemed) University, India. In addition, she is Visiting Faculty at IMT Ghaziabad, Research Professor at Alliance University, India, Mentor for the Doctor of Business Administration (DBA) programme at Rushford Business School, and Advisor with Connect IT Technologies. Across these roles, she is actively involved in doctoral mentoring, research reviews, academic evaluations, and industry–academia collaboration.
Dr. Gaur has previously 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 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 currently serves as Co-Editor-in-Chief of Communications in Statistics: Case Studies, Data Analysis and Applications (Taylor & Francis) and holds several editorial and reviewer roles across reputed international journals.
Recognized globally for her research impact, Dr. Gaur is listed among the Elsevier–Stanford World’s Top 2% Scientists (2024, 2025) and is a Senior Member of IEEE.
