Towards More Granular Reasoning: Mapping the DSA Transparency Database Taxonomy to Platform Terms and Conditions Using Large Language Models

📄 arXiv: 2610.26226v1 📥 PDF

作者: Alyson Ranucci, Eline Leijten, David Graus

分类: cs.AI, cs.CY

发布日期: 2026-10-19

备注: Accepted to JURIX 2026

💡 一句话要点

通过大语言模型映射DSA透明数据库分类以提升内容审核透明度

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 内容审核 数字服务法 大语言模型 政策分析 平台治理 透明度提升

📋 核心要点

  1. 现有DSA透明数据库的分类方法未能有效反映在线平台的条款与条件，导致内容审核决策的透明度不足。
  2. 本文提出通过GPT-4.1分类器对31个在线服务的条款进行映射，分析其与DSA-TDB分类的适用性。
  3. 研究发现不同服务类型的分类适用性差异显著，且分类重叠使得单一分类难以实现，提出了改进建议。

📝 摘要（中文）

根据数字服务法第24(5)条，在线平台提供商需向DSA透明数据库提交每一内容审核决策及其理由声明。该数据库的分类主要基于合同执行，然而其分类是否准确描述了平台的条款尚未得到验证。本文利用少量示例的GPT-4.1分类器，对31个在线服务的条款与DSA-TDB的高层分类进行映射分析，发现不同服务类型的分类适用性差异显著，并且子分类之间存在重叠，导致单一高层分类的分配困难。我们建议采用更具服务类型区分的分类方案，以改善现有的报告机制。

🔬 方法详解

问题定义：本文旨在解决DSA透明数据库分类与在线平台条款之间的映射问题。现有方法未能准确反映平台的条款，导致内容审核透明度不足。

核心思路：通过使用少量示例的GPT-4.1分类器，对31个在线服务的条款与DSA-TDB的高层分类进行映射，分析其适用性和重叠性。

技术框架：研究流程包括收集447份政策文件，利用GPT-4.1进行分类，并对分类结果进行分析，重点关注不同服务类型的分类适用性。

关键创新：最重要的创新在于通过大语言模型实现条款与分类的自动映射，揭示了现有分类设计的不足之处。

关键设计：在分类过程中，设置了高层分类标准，分析了不同服务类型的条款适用性，并提出了基于服务类型的改进建议。

🖼️ 关键图片

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📊 实验亮点

研究发现，电商平台的内容审核决策中，17.5%涉及不安全产品，而成人平台仅为0.2%。此外，Meta平台的公民话语负面影响达到9.9%，而其他平台则未见此类影响。这表明不同平台的审核决策存在显著差异，且现有分类存在重叠问题。

🎯 应用场景

该研究的潜在应用领域包括在线平台的内容审核机制、法律合规性检查及政策制定。通过提升内容审核的透明度，能够增强用户信任，促进平台的健康发展。未来，该方法可扩展到其他法律法规的合规性分析中。

📄 摘要（原文）

Article 24(5) of the Digital Services Act (DSA) requires providers of online platforms to submit every content moderation decision they make, with a corresponding Statement of Reasons (SoR), to the DSA Transparency Database (DSA-TDB). Such decisions are classified against seventeen high-level attribute categories that categorize the primary reason for the moderation decision. Because over 99.8% of statements cite a violation of the provider's own terms and conditions rather than a legal provision, the database is overwhelmingly a record of contractual enforcement using catch-all reporting; however, whether its categories actually describe those contractual rules well has not been examined. In this paper, we map the terms and conditions (T&C) of 31 designated online services against the DSA-TDB's high-level category taxonomy using a few-shot GPT-4.1 classifier over 447 policy documents. The analysis concerns how T&C rules are represented in the taxonomy, not how they are enforced. Two findings emerge. First, high-level category applicability varies sharply by service type: unsafe products account for 17.5% of e-commerce assignments and 0.2% of adult-platform assignments, and negative effects on civic discourse reaches 9.9% within the Meta family but is absent from adult platforms, Booking.com, and Wikipedia. Second, the categories have notable overlap among sub-category specifications, which makes assigning a single high-level category to a decision difficult. We argue that catch-all reporting is plausibly in part a consequence of the DSA-TDB's schema and attribute design, and recommend a schema that is differentiated by service type with more mandatory attributes derived inductively from platform governance language.