Beyond AI-Generated Labels: Watermarking, Co-Creation, and Conflation of AI-Generation with Disinformation

Technical Summary: Beyond "AI-Generated" Labels

Problem Statement
The paper addresses the growing reliance on watermarking and visible "AI-generated" labels as primary solutions for distinguishing synthetic content from human-created material. The authors argue that while watermarking is technically designed to trace model provenance, its operationalization into visible labels for individual, co-created outputs is conceptually and practically flawed. The core problem is twofold:

1. The Co-Creation Gap: Watermarking assumes a binary distinction between human and machine authorship. However, in generative AI workflows, human and AI contributions are interwoven in a continuum of iterative revision, selection, and framing. Current watermarking mechanisms cannot quantify degrees of human intervention or capture the relational nature of human-AI collaboration.
2. The Epistemic Conflation: There is a dangerous tendency in policy and public discourse to equate "AI-generated" with "inaccurate" or "disinformation." The paper posits that the synthetic nature of content does not inherently determine its truthfulness, intent, or ethical value. Conflating form (synthetic origin) with intent (deception) risks stigmatizing legitimate educational and creative uses of AI while fostering misplaced trust in unmarked content.

Methodology
This paper employs a conceptual and philosophical analysis rather than empirical experimentation. The authors' methodology involves:

• Technical Critique: Reviewing the limitations of invisible watermarking (e.g., susceptibility to stripping, corruption, and forgery) and the lack of interoperable standards across different AI models.
• Philosophical Inquiry: Re-examining the definition of authorship, drawing on theories from Foucault and Boden to argue that AI lacks moral and epistemic agency. The authors position AI as a tool that extends human reasoning rather than an autonomous co-author.
• Case Analysis: Utilizing illustrative examples (e.g., academic writing, newsroom workflows, artistic creation) and the specific case of the book Hypnocracy (co-written by Andrea Colamedici and AI) to demonstrate how binary labeling fails to capture the nuance of blended creative processes.
• Literature Synthesis: Integrating findings from human-computer interaction studies regarding user perception of ownership and existing research on the inability of humans and detectors to reliably distinguish AI text from human text.

Key Contributions
The paper makes several distinct contributions to the discourse on AI governance and ethics:

1. Reframing Authorship: It argues that authorship in the age of generative AI should be viewed as a transparent human practice involving oversight and accountability, rather than a binary indicator of machine involvement. The authors contend that treating AI as a "co-author" collapses the history of tool-mediated creativity.
2. Critique of Watermarking Efficacy: The authors demonstrate that invisible watermarks are fragile and binary (all-or-nothing), making them unsuitable for grading partial AI involvement. They highlight that watermarks encode model origin at the moment of generation but fail to track subsequent human editing, potentially misattributing heavily edited works as purely AI or purely human.
3. Decoupling Synthetic from Deceptive: A central contribution is the argument that "synthetic" does not equal "false." The paper asserts that disinformation is defined by the intent to deceive, not the method of creation. Automatically stigmatizing AI content risks hindering beneficial applications in public health, education, and accessibility.
4. Proposing Alternative Frameworks: Instead of relying on output-based detection, the paper advocates for:
   • Process Transparency: Disclosure mechanisms that clarify how and why AI was used (e.g., for brainstorming, editing, or formatting) and what human verification steps were taken.
   • Information Literacy: Shifting the burden of resilience from technical detection to human critical thinking, empowering individuals to evaluate the accuracy, intent, and rhetorical strategies of messages regardless of their origin.

Results and Findings
The analysis yields the following conclusions:

• Technical Limitations: Watermarks can be easily stripped, forged, or rendered undetectable by common edits (paraphrasing, resizing, translation). Furthermore, the lack of shared standards means detection remains fragmented.
• Misalignment with Practice: In co-creative workflows, the "degree" of AI contribution is fluid. A watermark that survives editing may falsely label a human-curated work as AI-generated, while a watermark that is erased may falsely suggest a work is entirely human-made.
• Stigmatization and False Confidence: Visible "AI-generated" labels may stigmatize legitimate uses of generative tools while creating a "false negative" problem where unmarked content is assumed to be authentic and safe.
• The Failure of Binary Classification: Attempts to neatly separate "AI content" from "human content" are fundamentally flawed in a landscape of pervasive human-AI collaboration. The boundary is porous, and labeling schemes that treat them as separable entities fail to reflect reality.

Significance
The paper's significance lies in its call to move beyond the "watermarking as a panacea" narrative. It argues that effective governance must differentiate between domains:

• Infrastructure Level: Watermarking may remain useful for identifying high-volume, fully automated outputs (e.g., bot networks, spam pipelines) where human scrutiny is impossible.
• Individual/Content Level: For creative and scholarly outputs, meaningful transparency depends on human-centered disclosure regarding process, intent, and verification, rather than binary labels.

Ultimately, the authors posit that the solution to AI-driven disinformation is not better detection of synthetic media, but the cultivation of information literacy and the maintenance of epistemic accountability through transparent human oversight. The paper suggests that focusing on the medium of creation distracts from the more critical evaluation of intent and truthfulness.