Decoding the Legalese: A Scalable and Quantitative Framework for Analyzing Corporate Privacy Policies
An LLM pipeline structures 10,000 privacy policies and scores transparency, protection, and business data use.
Researchers present an LLM pipeline that converts raw privacy policies into structured records of data elements, practices, and the relationships between them. They apply it to 10,000 website policies, describing the result as the most comprehensive dataset of its kind. New repeatable metrics score completeness, transparency, commitment to user protection, and emphasis on business-driven data practices. The measures support comparisons across sectors and of the tension between user protection and commercial interests.
- LLM pipeline extracts data elements and linked practices
- Applied to 10,000 website privacy policies
- Metrics cover completeness, transparency, protection, and business use
- Supports comparisons within and across industry sectors
Full article214 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.26680