Automated AI-generated content is increasingly used on social media,video-sharing, and user-generated content platforms to extract financialand other incentives through monetization systems, principally by minimizinghuman effort while exploiting platform reward structures. Thisgrowing volume of low-effort automated content is also increasingly capableof end effect manipulation — shaping viewer perception and beliefregardless of the factual accuracy of the content itself. Existing responsesto this problem are fragmented: individual platforms deploy their own detectionsystems, and the Content Authenticity Initiative’s C2PA standardprovides cryptographic provenance for content produced using complianttools. However, a structural gap remains. These approaches focus onidentifying and authenticating content after it is produced; none of themconstrain the ability of automated AI pipelines to generate such contentat scale in the first place. This paper proposes a framework grounded ina single foundational principle: human content creation, whether assistedby AI or not, leaves material traces throughout its production pipeline —artifacts that a purely automated pipeline cannot generate. The frameworkrequires creators seeking monetization to produce and submit suchmaterials for each piece of content, cryptographically linked to that contentthrough an independently verified attestation and certification process.By placing constraints at multiple points across this pipeline —economic, technical, and legal — the framework makes operating an automatedcontent pipeline at scale economically unviable and, in cases ofattempted circumvention, exposes operators to existing criminal liabilityfor document fraud. The result is a structural rather than detection-baseddeterrent, one that does not depend on improving classification accuracyas AI generation capability advances.
Nazeer Ali Syed (Wed,) studied this question.