AI RESEARCH
Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
arXiv CS.CL
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ArXi:2604.13776v1 Announce Type: cross 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 graphic groups. We examine how this content dependence creates modality-specific pathways to bias.