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.