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
From the original article
1 min read at arxiv.orgEven 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.
By Jiaming Tang · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.