Call for Chapters: Latest Advances in Explainability for Large Language Models

Editors

Mohit Mittal, Smart Labs AI GmbH, Germany
Swadha Gupta, CAI GmbH, Germany
Satendra Kumar, Meerut Institute of Engineering & Technology, India
Shaifali Chauhan, Angel One Ltd, India
Ganga Sharma, Manav Rachna University, India

Call for Chapters

Proposals Submission Deadline: June 18, 2026
 Full Chapters Due: October 1, 2026
 Submission Date: October 1, 2026

Introduction

The rapid advancement of artificial intelligence has transformed the fields of Large Language Models (LLMs), explainable AI, and computer vision, leading to intelligent systems that are increasingly capable, interactive, and impactful across real-world domains. From generative text systems and multimodal foundation models to interpretable vision systems and trustworthy decision-support tools, these technologies are reshaping the future of human-centered computing and automated intelligence. Despite major breakthroughs in model performance and scalability, important concerns remain around interpretability, transparency, fairness, accountability, robustness, hallucination, bias, and ethical deployment. As AI systems become more deeply integrated into critical sectors such as healthcare, education, finance, business, security, and public services, there is a growing need to understand not only how these models perform, but also how they make decisions and how they can be responsibly deployed. This book seeks to provide a comprehensive platform for researchers, scholars, and practitioners to contribute cutting-edge work that addresses recent developments in LLMs, explainability, and computer vision. It will bring together theoretical advances, applied research, frameworks, tools, case studies, and interdisciplinary perspectives that explore methods, applications, and challenges in these fast-evolving domains.

Objective

This book intends to present recent advances and emerging directions in Large Language Models, explainability, and computer vision by bringing together contributions from researchers and practitioners across academia and industry. The primary objective is to examine how modern AI systems can be designed, evaluated, and deployed in ways that are not only powerful and efficient, but also interpretable, trustworthy, ethical, and human-centered. The book aims to: 1. explore new methods and architectures in LLMs, explainable AI, and computer vision; 2. highlight the growing intersection of language, vision, and multimodal intelligence; 3. provide insights into transparency, fairness, robustness, uncertainty, and accountability in AI systems; 4. showcase real-world applications and deployment case studies across diverse sectors; 5. identify current limitations, research gaps, and future directions in explainable and trustworthy AI. By combining foundational knowledge with practical perspectives, this book will contribute to current research and further support the development of more reliable, interpretable, and socially responsible AI technologies.

Target Audience

This book is intended for researchers, academicians, scientists, postgraduate students, doctoral scholars, AI engineers, data scientists, machine learning practitioners, and professionals working in artificial intelligence, natural language processing, computer vision, explainable AI, multimodal systems, and trustworthy AI. The book will also be valuable to industry experts, innovation leaders, policy researchers, and decision-makers interested in understanding and applying advanced AI systems in real-world environments. Readers from domains such as healthcare, education, finance, business analytics, cybersecurity, robotics, and intelligent automation will benefit from the research, methodologies, and applications presented in this volume.

Recommended Topics

Contributors are invited to submit chapters on topics including, but not limited to, the following: Large Language Models: architectures, training strategies, and emerging trends Explainable AI for Large Language Models Interpretability and transparency in foundation models Explainable computer vision systems Vision-language models and multimodal AI Human-centered and trustworthy AI Hallucination, uncertainty, and reliability in generative AI Bias, fairness, and ethical issues in LLMs and vision systems Attention, saliency, and feature attribution techniques Interpretable deep learning methods Explainability in image classification, object detection, and segmentation Multimodal reasoning and cross-modal intelligence Explainable conversational AI and intelligent assistants Robustness, adversarial challenges, and model evaluation AI governance, accountability, and responsible deployment Explainable AI in healthcare applications Explainable AI in business, management, and decision analytics Computer vision for smart systems, security, and industrial applications Educational and societal applications of explainable AI Case studies, tools, frameworks, and benchmark datasets in explainable and multimodal AI

Submission Procedure

Researchers and practitioners are invited to submit on or before June 18, 2026, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by July 2, 2026 about the status of their proposals and sent chapter guidelines.Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by October 1, 2026, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Latest Advances in Explainability for Large Language Models. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.

Publisher

This book is scheduled to be published by IGI Global Scientific Publishing, an international academic publisher of the "Information Science Reference", "Medical Information Science Reference", "Business Science Reference", and "Engineering Science Reference" imprints. IGI Global Scientific Publishing specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2027.

Indexing Information for Prospective Authors

IGI Global Scientific Publishing meets the criteria for inclusion in major indexing services such as Scopus; however, it is important to note that all indexing decisions are made independently by these services. IGI Global Scientific Publishing books are selectively indexed by the indexing organization after publication. Indexing cannot be guaranteed for any book prior to publication, and the indexing organization has complete control over the final selection and timeline.

Important Dates

June 18, 2026: Proposal Submission Deadline
July 2, 2026: Notification of Acceptance
October 1, 2026: Full Chapter Submission
December 3, 2026: Review Results Returned
January 14, 2027: Final Acceptance Notification
January 28, 2027: Final Chapter Submission

Inquiries

Mohit Mittal
Smart Labs AI GmbH
mittal.mohit02@gmail.com



Swadha Gupta
CAI GmbH
swadhagupta15@gmail.com



Satendra Kumar
Meerut Institute of Engineering & Technology
satendra04cs41@gmail.com



Shaifali Chauhan
Angel One Ltd
shaifalichauhan18nov@gmail.com



Ganga Sharma
Manav Rachna University
drgangasharma80@gmail.com
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