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Readers can't identify watermarked AI text
In a recent informal test, a creator examined public perceptions of AI watermarking by having participants identify watermarked and unwatermarked responses. The results indicated that most readers failed to distinguish between the two, suggesting that watermarking may not detract from content quality as commonly assumed. Despite limitations, such as being non-scientific, this test highlights the need to reconsider how watermarking affects AI text generation.
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Published 21 Aug 2026, 20:35 UTC2 min read
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Summary
TL;DR
- Participants scored an average of 3.92 out of 10 in identifying watermarked content.
- The first choice bias led to skewed scores, emphasizing lack of identification ability.
- The creator used Qwen3-30B-A3B-Instruct-2507 for generating responses.
- The results provoke thought on consumer concerns regarding watermark AI outputs.
- Watermarking may not impact text quality, countering prevalent concerns.
Interesting facts
- Random selection led to expected scores, showing participants' unfamiliarity with identifying watermarks.
- The quiz methodology was informal and based on casual testing conditions.