System analyzes corporate privacy policies using large language models
Decoding the Legalese: A Scalable and Quantitative Framework for Analyzing Corporate Privacy Policies
Cryptography and SecurityComputers and Society
Summary
Privacy policies are often long and full of legal words, making them hard for most people to understand. The authors created a system that uses advanced AI language models to turn these policies into easy-to-read data, highlighting important parts like what information is collected and how it is used. They studied 10,000 policies, making one of the biggest collections ever, and developed clear ways to measure how complete, transparent, and user-focused these policies are. This helps compare companies and industries on how well they protect user privacy versus pursue business goals.
What this means in practice
- •For privacy compliance teams: Automatically generate detailed summaries and metrics for company privacy policies to assess and improve regulatory adherence and transparency.
- •For legal technology developers: Build tools that convert complex privacy policy text into structured data for easier policy comparison and user communication.$Commercial implications: Enables development of commercial software products that analyze and present privacy policies in user-friendly formats.
Authors
Jiaming Tang, Chenlan Wang, Mingyan Liu, Armin Sarabi
Abstract
Even though privacy policies are the primary mechanism organizations use to disclose how they collect, process, and share personal data, they are difficult for average users to interpret, perhaps by design, due to their verbosity and dense legal language. Importantly, there is a lack of standardized metrics that characterize key qualities of a privacy policy beyond regulatory requirements. Recent advances in large language models (LLMs) make it feasible to automatically structure and analyze these documents at scale. In this study, we develop and evaluate an end-to-end, LLM-enabled system that converts raw privacy policies into fine-grained structured representations and a set of quantitative measures. Our pipeline applies a detailed taxonomy to extract specific data elements and governing practices, capturing relational links that connect each practice to the data elements it references. We apply our framework to a diverse corpus of 10,000 website privacy policies, yielding, to the best of our knowledge, the most comprehensive dataset of its kind to date. Building on our structured representations, we introduce the first standardized and repeatable quantitative metrics for evaluating privacy policies along four dimensions: completeness, transparency, commitment to user protection, and emphasis on business-driven data practices. This allows us to compare policies within and across industry sectors, and to assess the tension between user protection and business interests.