法律 AI // 资讯总览
需要被持续追踪的
法律 AI 信号。
本期索引按监管、判例、实务与研究归档；可依主题或关键字定位已发布记录。
法律 AI // 资讯总览
本期索引按监管、判例、实务与研究归档；可依主题或关键字定位已发布记录。
原题: Decoding the Legalese: A Scalable and Quantitative Framework for Analyzing Corporate Privacy Policies
涉及法律行业大模型应用、律所与企业法务数智化实践指引。
arXiv预印本论文《Decoding the Legalese: A Scalable and Quantitative Framework for Analyzing Corporate Privacy Policies》提出一套由大语言模型驱动的隐私政策自动分析系统。研究背景是，隐私政策虽是组织披露个人数据收集、处理和共享方式的主要文件，却普遍篇幅冗长、法律术语密集，普通用户难以理解，且现有研究缺少超越监管合规要求、能够重复测量政策质量的标准化指标。该系统通过细化分类体系，将原始隐私政策转换为结构化数据，提取具体数据要素、处理和治理实践，并建立实践与所涉数据要素之间的关联。研究团队进一步将框架应用于1万份网站隐私政策，形成据称同类中最全面的数据集，并从完整性、透明度、用户保护承诺以及商业导向数据实践强调程度四个维度提出可比较、可重复的量化指标，用于跨行业评估用户保护与商业利益之间的张力。论文属于学术研究，不提出新的法律条文、监管命令或施行过渡期。
展示来源全文，完整内容以原文页面为准。
arXiv:2609.26680v1 Announce Type: cross 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.
研究的核心法律焦点并非改变既有隐私法义务，而是把企业隐私政策中的告知范围、数据用途、共享关系和保护承诺转化为可计算指标。其潜在价值在于辅助判断告知是否完整、透明及是否过度偏向商业利用，但量化结果不能当然等同于合法性结论。隐私政策通常同时承担告知、合同解释和合规证明功能，模型提取错误、语境遗漏或指标权重偏差，可能影响企业责任认定、用户理解及监管审查，因此仍须区分文本质量、实际处理行为与法律合规责任。
文书与研报规范引注
《大模型量化分析企业隐私政策框架发布》（来源: arXiv cs.CY，发布日期: 2026/09/27，访问链接: https://arxiv.org/abs/2609.26680）