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 connect these to the governance frameworks currently mandating watermarking deployment and show that watermarking is held to a lower fairness standard than the generative systems it is meant to govern. 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：水印正成为人工智能内容认证的默认机制，治理政策与框架将其引述为内容溯源的基础设施。然而，在文本、图像与音频模态中，水印信号强度、可检测性与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、文化视觉传统及人口群体间存在系统性差异。我们探讨了这种内容依赖性如何产生跨模态的偏差路径。通过审视各模态的主要水印基准测试，我们发现除一项例外，均未报告跨语言、文化内容类型或人群组别的性能表现。为此，我们提出水印多元化基准测试的三个具体评估维度：跨语言检测公平性、多元文化内容覆盖率以及检测指标的人口统计学细分。我们将这些维度与当前强制部署水印的治理框架相关联，并表明水印所遵循的公平性标准低于其本应治理的生成式系统。我们的立场是：评估必须优先于部署，且应用于人工智能模型的偏差审计要求应延伸至验证层。