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Anthropic to watermark AI-generated text from Claude, including edited human writing

Anthropic will introduce invisible watermarks for text generated by its Claude AI models, a measure that will also apply to human-written content edited by Claude. This initiative is in response to new EU regulations but will be implemented globally.

  • Invisible watermarks will be added to text generated by Claude models launched in the EU from 2 August.
  • The watermarking system will also mark some human-written text if it has been edited or processed by Claude.
  • Anthropic will provide tools for users and third parties to check for these marks, though technical details are not yet public.

Anthropic is set to implement invisible watermarks on text produced by its Claude AI models. This change, which began with models launched in the EU from 2 August, is part of new EU rules aimed at making AI-generated content easier to identify.

The Silicon Valley AI company confirmed on Tuesday that these machine-readable marks will apply worldwide wherever supported Claude models, including Claude, Claude Code, and its developer platform, are available. The embedded watermarks are designed to persist through copying and pasting, and potentially some editing.

However, the system will also mark some text originally created by humans if it has subsequently been edited or processed by Claude. Anthropic acknowledged that the presence of a watermark does not definitively mean Claude authored the original material, as it could appear on an article proofread or translated by the AI.

Anthropic has cautioned against using the marks as conclusive proof of AI authorship, noting that heavy editing or paraphrasing can make watermarks undetectable. Short passages may also lack sufficient text for a reliable signal, and metadata on images can disappear during file conversion.

Why this matters: The introduction of watermarks aims to increase transparency regarding AI-generated content, but the marking of human-written text edited by AI could complicate efforts to distinguish between human and machine authorship.

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