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.

翻译：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，而这些特性在语言、文化视觉传统和人口群体间存在系统性差异。我们考察了这种内容依赖性如何产生模态特定的偏差路径。通过审查各模态的主要水印基准，我们发现，除一个例外，所有基准均未报告跨语言、跨文化内容类型或跨群体性能。为解决此问题，我们提出了水印多元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。我们主张水印是多元对齐管线的一部分，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，而适用于AI模型的偏差审计要求应延伸至验证层。