This book examines how privacy policies have become objects of computational analysis and what that development means for privacy governance. It traces the field over roughly two decades, following the progression from early rule-based and lexicon-driven systems to machine learning, deep learning, and large language models.  This book also reviews the major tasks that have defined automated privacy policy analysis, including classification of data practices, information extraction, corpus and taxonomy development, coverage analysis, compliance-oriented review, and user-facing explanation, while situating privacy policies within a broader accountability ecosystem of contracts, consent flows, governance records, and technical traces.

Bringing together research in natural language processing, machine learning, privacy law, and governance practice, this book assesses both what these methods have made possible and where they remain limited. It shows how technical progress has enabled large-scale analysis of privacy documents, but also why prediction alone is not enough for legally and organizationally dependable use. 

This book targets researchers, regulators, auditors, civil-society organizations, and industry practitioners. It provides a structured account of the field and identifies the central challenges that must be addressed for automated privacy policy analysis to support real accountability and decision-making.

Rinku Dewri is a faculty member in the Department of Computer Science at the University of Denver whose research sits at the intersection of privacy, security, and applied machine learning. His work on privacy includes technical foundations for privacy-preserving data systems, spanning database privacy, location privacy, private record linkage, and formal trade-offs between privacy and utility, as well as studies of how privacy policies are written, structured, and change over time. In recent work, he has examined longitudinal shifts in privacy policy readability and organization, proposed NLP methods to identify and characterize policy changes, and developed approaches to extract structured semantic information to support privacy policy comprehension. He has also analyzed limitations that arise when large language models are used to assist with interpreting privacy documents, emphasizing the risk of mismatches between generated explanations and the underlying text. Across these contributions, his research aims to make privacy policy analysis reliable and interpretable for privacy governance by producing representations and measurements that better support informed decision-making and oversight.