Unlocking Unstructured Data
Description
This book explores how Large Language Models can help public organizations turn previously unusable information into actionable insight. Government agencies collect enormous volumes of handwritten forms, PDFs, free-text responses, case notes, and other unstructured content, yet much of it remains difficult to analyze at scale. This book shows how LLMs, combined with OCR, computer vision, and related document-processing techniques, can extract structure and meaning from these data sources, helping public sector teams improve service delivery, operational efficiency, and evidence-based decision-making. It is written for data scientists, AI practitioners, public administrators, policymakers, researchers, and graduate students who want a practical and accessible guide to this fast-emerging field.
Unlocking Unstructured Data: Transforming Public Services with Large Language Models offers a distinctive public-sector perspective on both the technical and organizational challenges of deploying LLMs responsibly. It examines foundational concepts, implementation architectures, and evaluation frameworks, then moves into real-world case studies across healthcare, social services, taxation, regulatory compliance, and citizen engagement. Readers will also find guidance on governance, privacy, explainability, bias mitigation, and change management, making this a useful resource for anyone seeking to modernize government data workflows while maintaining trust, transparency, and accountability.
More details
Person
Dr. Hossein Mohammadi Rouzbahani is a distinguished researcher and professional currently advancing the frontiers of artificial intelligence and energy systems. With a Ph.D. in Electrical and Computer Engineering from the University of Calgary, he has built a robust academic foundation, specializing in the Internet of Energy, Reinforcement Learning, Smart Grids, Electricity Markets, and Cyber-Physical Systems security. Now, as a postdoctoral researcher at the University of Calgary, he focuses on applying AI to public sector innovation, exploring the intersection of data science, public administration, and the governance of emerging technologies, with a keen interest in enhancing government services through digital transformation. In addition to his academic pursuits, Hossein serves as a Lead Data Scientist for the Canadian government at Children, Community and Social Services in the Greater Toronto Area. Here, he leverages his expertise in machine learning and natural language processing to improve public service delivery while upholding democratic values and citizen trust.
Content
.- The Unstructured Data Challenge in Public Services.
.- Large Language Models: Capabilities and Limitations.
.- Framework for Evaluating LLM Applications in Public Services.
.- Document Digitization Pipelines.
.- Form Processing and Information Extraction.
.- Free-Text Analysis for Citizen Feedback and Communications.
.- Multimodal Approaches for Complex Document Understanding.
.- Healthcare Record Digitization and Analysis.
.- Social Services and Benefits Administration.
.- Regulatory Compliance and Documentation.
.- Public Feedback and Citizen Engagement.
.- Governance Frameworks for LLM Implementation.
.- Privacy, Security, and Trust.
.- Implementation Strategies and Organizational Change.
.- Future Directions and Research Agenda.