NO PASSING OFF

> Preventing the Passing Off of AI Content as Human Work This page explores the development of a **rule**—a practical and enforceable governance procedure—to address the challenge of distinguishing human-authored content from AI-generated content.

- Digital signatures and Proof of human. The signing of content prior to any assumption that it is not ai generated xxx.

- Yuval Harari is a good spokesperson for being clear about what is and what is not human authored. His voice can help define this NO PASSING OFF definition.

- We imagine a research group around this question. The creation of research wiki and then a Guide. We then imagine runiing micro-assemblies to test out and capture the ethical considerations of diverse and representative audiences.

- Finally we imagine, trining AI on this material, and beginning to run sustainable workshops both fictional and real to generate a new legal commons around a body of case law that considers the question - is thi passing off?

- AI can then be used as a low cost judge of whether passing-off has occured, Humans can dispute this claim, and a sustainably financed arbitration prcess can judicate, adding to the body of case law.

# Digital Signatures and Proof of Humanity Before any content can be presumed human-authored, it should be accompanied by a verifiable digital signature or a form of proof of humanity. These might include cryptographic signatures, biometric verification, or other verifiable attestations that link the content to a real person. This step helps ensure that authorship claims can be trusted, particularly in high-stakes domains.

## Defining No Passing Off The principle of No Passing Off means that no one should misrepresent AI-generated work as if it were created by a human. Public thinkers like Yuval Noah Harari have articulated the urgency of this distinction. Their voices can help define and legitimize the cultural and legal basis of this emerging rule.

# A Collaborative Research Group and Ethical Guide We propose the formation of a dedicated research group to maintain a living Research Wiki and produce a publicly editable Ethical Guide titled something like *Ethical Authorship in the Age of AI*. This guide would document best practices, edge cases, and community-sourced strategies to prevent AI-generated content from being passed off as human-made.

# Participatory Micro-Assemblies To ground this work in inclusive and diverse perspectives, we envision hosting micro-assemblies—small, deliberative public forums drawn from a broad demographic. These assemblies would debate and clarify ethical boundaries and help refine the rule through iterative community engagement.

# Training AI Judges and the Legal Commons As our body of research and case studies grows, it could be used to train AI judges—low-cost, automated evaluators that offer first-pass assessments of whether passing-off may have occurred. These judgments would not be final. Instead, humans could initiate dispute processes through a transparent and sustainably financed arbitration system. - AI Judges and Legal Commons - Robot Judges

The outcomes of these disputes would feed into a growing public database of decisions, forming a new legal commons—a shareable, open set of precedents for future cases.

# Hosting Workshops and Simulations To test and iterate this rule further, we imagine running both fictional (simulated) and real-world workshops. These could explore hypothetical cases, help train participants, and generate new insights. Over time, this effort may help establish an enforceable, broadly accepted framework for distinguishing and managing AI-generated content.