The author proposes methods for indicating when content is generated by AI, emphasizing politeness to readers and preventing LLMs from polluting their own training data. One suggestion involves using a specific language tag, such as `en-AI` or a private use tag like `en-GB-x-AI-deepblue`, though standardization is noted as a slow process. Another approach leverages HTML elements like `<q>` or `<blockquote>` with a `cite` attribute pointing to the AI source, or the `<samp>` element which is designed for program output. The author also suggests augmenting these with Schema.org metadata to semantically identify the AI author. AI
IMPACT Proposes methods for transparently labeling AI-generated content, potentially improving user trust and data integrity.
RANK_REASON Blog post discussing potential technical solutions for AI content disclosure.
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