Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
For policymakers and developers deploying watermarking for content authentication, the paper highlights a critical fairness gap in evaluation that could lead to biased enforcement across global populations.
The paper identifies that AI content watermarking performance varies across languages, cultures, and demographic groups, yet major benchmarks fail to report such disparities. It proposes three evaluation dimensions for pluralistic benchmarking and argues that watermarking should meet the same fairness standards as generative AI models.
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.