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RSL Media launches Human Consent Standard for AI licensing

By LDS Team · How we report||
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RSL Media launches Human Consent Standard for AI licensing

According to The Verge, nonprofit RSL Media on May 12, 2026 unveiled the Human Consent Standard, a machine-readable licensing framework that lets people set terms for how AI systems may use their likenesses, creative works, characters, and designs. The Verge reports the standard is backed by talent including George Clooney, Tom Hanks, Meryl Streep, Viola Davis, Kristen Stewart, and Steven Soderbergh, and by organizations such as the Creative Artists Agency and Music Artists Coalition. In an email to The Verge, RSL Media cofounder Eckart Walther said the Human Consent Standard builds on the existing RSL Standard and can be discovered by AI crawlers through machine-readable web signals, and that it applies to an underlying work or identity "wherever it appears," rather than only at a specific URL.

What happened

According to The Verge, nonprofit RSL Media announced the Human Consent Standard, a new machine-readable licensing framework that lets people set terms governing how AI systems may use their likenesses, creative works, characters, and designs. The Verge reports the launch is backed by public figures including George Clooney, Tom Hanks, and Meryl Streep, as well as industry groups such as the Creative Artists Agency and the Music Artists Coalition. The Verge also reports that RSL Media cofounder Eckart Walther, in an email to The Verge, said the new standard builds on the existing RSL Standard and can be discovered by AI crawlers via machine-readable web signals; Walther wrote that the Human Consent Standard "applies to the underlying work, identity, character, or mark itself, wherever it appears."

Technical details

Editorial analysis - technical context: Public reporting indicates the Human Consent Standard follows the wider pattern of machine-readable metadata approaches used to communicate permissions to crawlers and automated systems; similar schemes historically rely on structured tags, headers, or registry entries to encode licensing rules. For practitioners, the critical technical question is how broadly and consistently AI developers will implement detection and enforcement of such signals, and whether the standard includes validation, provenance, or dispute-resolution mechanisms to handle conflicting signals.