The Irony of AI Governance: When the Tool Helps Write Its Own Rules
Naming the Paradox
There’s a paradox at the heart of my work: I use AI systems, including ChatGPT, to draft legal disclaimers, governance frameworks, and research reflections. In other words, I ask AI to help write the very rules that are meant to govern AI.
At first glance, this feels absurd. Governance is supposed to stand outside power, holding it accountable. Isn’t this like asking a fox to design the locks for the henhouse? Or asking fire itself to draft the fire-safety regulations?
But the more I sit with this irony, the more I see it not as a flaw but as the point.
The Fox in the Henhouse, Already
The uncomfortable truth is that AI is already inside the processes of governance.
Lawyers are testing AI to review contracts.
Regulators are experimenting with AI-driven audits.
Policy think tanks are drafting memos with model assistance.
If we pretend that governance can be designed in some pure vacuum, untouched by the very tools it seeks to regulate, we are only building frameworks on illusion.
The irony of AI co-writing governance is not an exception. It is the rule, hidden in plain sight.
Originality vs. Derivation
The paradox goes deeper still: authorship.
Large language models remix and synthesize. They are trained on vast mixtures of public text, licensed content, and human work. They do not “own” their outputs in the way a human author might.
And yet, here I am using such systems to help draft documents about intellectual property, originality, and authorship. How can I credibly write about protecting authorship using a tool that is itself derivative?
The answer, I think, lies in reframing authorship. Authorship is not isolation. Authorship is responsibility.
The machine can generate, but only the human decides what to keep, what to revise, and what to stand behind. Authorship lives not in the act of production, but in the act of judgment.
Governance as Recursion
Governance has long been imagined as top-down: rules applied to a domain from the outside.
But when AI helps write the rules for AI, governance becomes recursive. The tool is both the subject and the instrument of regulation.
Uncomfortable? Absolutely. Necessary? I would argue yes.
Governance is not static. It is dynamic, adaptive, and embedded in real workflows. If AI is already part of how governance happens, then governance frameworks must themselves be tested within this recursive reality.
By exposing the recursion instead of hiding it, we make governance more honest—and ultimately, more resilient.
Why the Irony Strengthens, Not Weakens
Critics might see this paradox as hypocrisy. I see it as methodology.
Judgment remains human: AI can draft, but it cannot take responsibility. That burden stays with me.
Transparency builds trust: Admitting that AI assists in drafting governance makes the process more credible, not less.
Recursion mirrors reality: Since AI is already co-writing rules through the workflows of regulators and companies, acknowledging the recursion aligns governance with the world as it is—not as we imagine it.
The irony is not a loophole. It is a stress test.
Owning the Paradox
The sharpest question at the heart of this critique is:
How can you credibly write about ethics, ownership, and governance while using AI tools that remix the work of others?
My answer: by owning the paradox, not erasing it.
Irony is not a weakness to be scrubbed out. It is a feature worth studying. It reflects the entangled reality of AI today: tools and rules, authors and derivatives, humans and machines, all co-producing meaning and control.
If governance is to be more than paper compliance—if it is to be real, dynamic, and trustworthy—it has to begin here, in the paradox.
Closing Reflections: Recursion as Reality
This post is less about resolving the irony than about inhabiting it.
By asking AI to help draft governance, I am holding up a mirror. The mirror shows how deeply AI is already embedded in authorship and accountability. It shows how impossible it is to separate “the tool” from “the rules.”
Yes, it is recursive. But recursion is the reality we live in. Governance must adapt, not by denying the irony but by treating it as the very ground on which trust can be built.
Key Concepts and Working Terms
Governance Paradox: The irony of using AI to help draft the very rules that govern AI—akin to asking a fox to design the henhouse locks.
Originality vs. Derivation: The tension between human authorship (which demands responsibility) and AI outputs (which remix and synthesize without ownership).
Authorship as Responsibility: A reframing where authorship is not defined by isolation but by the human judgment to take responsibility for generated content.
Governance as Recursion: The idea that when AI helps write governance for AI, the process becomes recursive—AI is both subject and instrument of regulation.
Irony as Stress Test: The notion that irony in governance design is not hypocrisy but a way to reveal, test, and strengthen the systems we build.
