AI content watermarking exhibits detection disparities across languages, cultures, and demographics due to content-dependent signal properties, with benchmarks failing to disaggregate performance and watermarking held to lower fairness standards than generative models.
Towards ge- ographic inclusion in the evaluation of text-to-image models
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Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
AI content watermarking exhibits detection disparities across languages, cultures, and demographics due to content-dependent signal properties, with benchmarks failing to disaggregate performance and watermarking held to lower fairness standards than generative models.