淹没在AI垃圾内容中：欧盟法律下社交媒体平台如何（不）标注AI和深度伪造内容
Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law
AI总结：
本研究基于欧盟法律审计四大社交媒体平台的AI标签实践，发现深度伪造标注覆盖率低（33%），并提出改进标签采用率、清晰度和有效性的具体机会。
AI中文摘要：
AI标签正成为社交媒体上AI生成内容透明度的主要保障措施，包括在欧盟《数字服务法》和《人工智能法》下。然而，关于平台在实践中如何实施此类标签的系统性证据仍然有限。我们依据欧盟委员会2026年7月关于深度伪造的指南，对Instagram、TikTok、X和YouTube上的AI标签进行了具有法律依据的审计。为此，我们分析了平台政策和检测方法，通过系统性风险和AI相关关键词收集了10,722条帖子，一个由专家标注的500条帖子子集，以及向四个平台受控上传的来自十种流行生成式AI工具的输出。我们发现AI标签现已广泛建立。所有四个平台都自动应用标签，且平台应用的标签比例高于早期审计。然而，在标签最关键的领域，覆盖仍不完整：在系统性风险情境中，专家识别的深度伪造中仅有33%带有平台应用的AI标签，而这些内容的中位观看次数达到160,000次。在受控上传的带有标准AI来源信号的AI生成内容中，平台仅标注了61%的上传内容，并且通常在上传后移除这些信号。总体而言，平台规则、标签设计、检测方法和报告方式存在显著差异。对此，我们提出了具体机会以改进AI标签的采用率、清晰度和有效性。
英文摘要：
AI labels are emerging as a primary safeguard for transparency about AI-generated content on social media, including under the EU Digital Services Act and AI Act. Yet, limited systematic evidence exists on how platforms implement such labels in practice. We conduct a legally grounded audit of AI labelling across Instagram, TikTok, X, and YouTube, drawing on the European Commission's July 2026 guidelines on deepfakes. To this end, we analyse platform policies and detection approaches, 10,722 posts collected via systemic-risk and AI-related keywords, an expert-annotated subset of 500 posts, and controlled uploads to the four platforms of outputs from ten popular generative AI tools. We find that AI labelling is now broadly established. All four platforms apply labels automatically and a greater share of labels are platform-applied than in earlier audits. However, coverage remains incomplete where labelling arguably matters most: only 33% of expert-identified deepfakes in systemic risk contexts carried a platform-applied AI label, while reaching a median of 160,000 views. In controlled uploads of AI-generated content carrying standard AI provenance signals, platforms labelled only 61% of uploads, and commonly strip those signals after uploading. Overall, platform rules, label designs, detection approaches, and reporting diverge substantially. In response, we identify concrete opportunities to improve the uptake, clarity and efficacy of AI labelling.
