谁被标记？AI内容水印中的多元评估差距
Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
AI总结：
本文揭示AI内容水印在不同语言、文化和群体间存在系统性偏差，提出跨语言检测一致性、文化多样性覆盖和检测指标人口统计分解三个评估维度，主张水印部署前必须进行公平性审计。
摘要：
水印正成为AI内容认证的默认机制，治理政策和框架将其引用为内容溯源的基础设施。然而，在文本、图像和音频模态中，水印信号强度、可检测性和鲁棒性取决于内容本身的统计特性，而这些特性在不同语言、文化视觉传统和人口统计群体间存在系统性差异。我们研究了这种内容依赖性如何产生特定模态的偏差路径。通过回顾各模态的主要水印基准，我们发现除一个例外，没有基准报告跨语言、文化内容类型或人群组的性能。为解决此问题，我们提出了多元水印基准测试的三个具体评估维度：跨语言检测一致性、文化多样性内容覆盖以及检测指标的人口统计分解。我们认为水印是多元对齐管道的一部分，应遵循相同的评估标准。我们将此与当前强制部署水印但未要求公平性评估的治理框架联系起来。我们的立场是评估必须先于部署，并且应用于AI模型的相同偏差审计要求应扩展到验证层。
英文摘要：
Watermarking is becoming the default mechanism for AI content authentication, with governance policies and frameworks referencing it as infrastructure for content provenance. Yet across text, image, and audio modalities, watermark signal strength, detectability, and robustness depend on statistical properties of the content itself, properties that vary systematically across languages, cultural visual traditions, and demographic groups. We examine how this content dependence creates modality-specific pathways to bias. Reviewing the major watermarking benchmarks across modalities, we find that, with one exception, none report performance across languages, cultural content types, or population groups. To address this, we propose three concrete evaluation dimensions for pluralistic watermark benchmarking: cross-lingual detection parity, culturally diverse content coverage, and demographic disaggregation of detection metrics. We argue that watermarking is part of the pluralistic alignment pipeline and should be held to the same evaluation standards. We connect this to governance frameworks currently mandating watermarking deployment without requiring fairness evaluation. Our position is that evaluation must precede deployment, and that the same bias auditing requirements applied to AI models should extend to the verification layer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
