Open Letter on AI-Assisted Writing
Elin Nguyen - January, 2026
Dear moderators, administrators, and stewards of online communities and institutions,
This letter addresses a fundamental issue in how epistemic communities and institutions are responding to AI-assisted writing—one that will determine whether moderation policies protect epistemic integrity or accidentally undermine it.
There is a growing fear of "AI-written text." Large language models have made it easy to generate fluent prose at scale: plausible arguments, polished summaries, confident explanations. These outputs can be produced with little care, little understanding, and little accountability. Any community built around epistemic rigor is right to worry about being flooded with high-fluency, low-integrity content.
To be explicit: I fully support strict filtering of spam, persuasion, flooding, and low-accountability participation. My claim is only that "AI-written" is the wrong proxy for these failure modes.
Disclaimer (AI assistance + authorship)
This text was written with AI assistance. More precisely: a language model generated draft phrasing, and I selected, edited, and finalized the content. I fully own the intent, the claims, and the responsibility for every sentence.
For clarity—because this is the core category error in this entire debate—large language models are not authors. They do not have beliefs. They do not have intent. They are not sentient being hiding in silicone plotting to take over the world. They cannot “mean” anything. They do not decide what is true and cannot take responsibility.
At the most literal level, what a language model does is: predict the next token (the next word or word-fragment) based on the preceding text. That’s it. Everything else—purpose, direction, judgment, accountability—comes only from the human.
The current framing—treating AI-assisted text itself as the primary epistemic risk—contains a category error.
The central risk is not synthetic language. The central risk is the silent transfer of epistemic authority away from humans—what I call oracle illusion: treating a fluent output as if it carried knowledge, judgment, or responsibility independent of a human author.
Language models do not hold beliefs. They do not assert claims. They do not have intent. They do not take responsibility. They generate candidate text conditioned on input. The epistemic failure occurs only when humans stop fully owning what they publish—when they cannot explain it, defend it, revise it, or bear responsibility for it.
Two questions that determine whether moderation protects or undermines epistemic integrity
1) Are you filtering out bad faith—or filtering out humans who now have access to expert patterns?
These are not the same thing.
Bad faith looks like:
low accountability
unwillingness to engage
refusal to revise
persuasion without falsifiability
spam, flooding, manipulation
Pattern access looks like:
better structure
cleaner prose
stronger synthesis
more legible arguments
fewer language barriers
If a filter collapses these into one bucket ("AI-written"), it will inevitably reject legitimate human participation—especially from those who reason well but write unevenly.
2) What are you implying about AI when "AI-written" is treated as inherently invalid?
That framing implicitly grants the tool a kind of authorship—sometimes even agency.
This is the paradox: You reject AI-assisted text as illegitimate while treating the tool as an author.
If a human chooses the intent, shapes the argument, edits, revises, and stands behind the claim, then the human is the author. If the community treats the presence of AI assistance as disqualifying by default, it isn't merely discouraging sloppy content—it is smuggling in the idea that the tool itself carries epistemic status (either contaminating the text or owning it). That is oracle illusion in reverse.
Even if no one explicitly believes models are agents, moderation that treats AI assistance as intrinsically disqualifying functions as if agency were present—because it assigns epistemic status (contamination or authorship) to the tool rather than to the accountable human.
These two questions matter because moderation decisions are not only "quality control." They also define what the community considers legitimate participation and legitimate cognition.
Why "AI-written" is the wrong epistemic target
(A) It confuses medium with integrity
"AI-assisted" is a method of production, not a property of truth.
A bad argument can be:
human-written
AI-assisted
editor-assisted
peer-review-assisted
A good argument can be:
non-native English
neurodivergent style
informal writing
highly polished writing
The epistemic question is never "how was it produced?" The epistemic question is: is the reasoning accountable and falsifiable?
If a community makes production aesthetics a stand-in for epistemic integrity, it will reject signal and admit noise, simply in different packaging.
(B) It creates a new gatekeeping layer
Rejecting content based on suspected AI assistance disproportionately excludes:
non-native English speakers
people with uneven writing mechanics
neurodivergent thinkers
technically brilliant authors who do not write in the "ritual voice" of the community
This is not hypothetical. English fluency and academic tone have always been gatekeeping layers in global intellectual discourse. AI tools simply made that gatekeeping visible by lowering the cost of clarity and structure.
If communities respond by policing style or tooling instead of integrity, the result is not epistemic rigor—it is aesthetic enforcement of "who is allowed to sound competent."
(C) Automated AI detection recreates oracle illusion
Using AI detection tools as an epistemic filter introduces the very dynamic epistemic communities aim to avoid: deferring judgment to an opaque, fallible system rather than engaging directly with arguments, methods, and evidence.
Even if the tool were "pretty good," the epistemic stance is wrong: it shifts the locus of judgment from accountable humans to an unaccountable classifier. That is oracle illusion as moderation policy.
And it is unstable: detection becomes an arms race, and false positives punish legitimate authors while bad actors adapt.
(D) It doesn't actually solve the problem it claims to solve
The stated target is often "AI slop." But slop predates AI. Poor thinking has always existed. The correct solution was never "ban the medium." The correct filter was always:
Can the author explain what they claim?
Can they defend it under scrutiny?
Can they revise it when wrong?
Is the reasoning falsifiable?
Are sources, assumptions, and uncertainty explicit?
Those are the real epistemic invariants.
A clearer standard (and one that's harder to game)
If the goal is epistemic integrity, then the standard worth enforcing is not "no AI-assisted text," but:
Humans must own intent.
Humans must understand what they publish.
Humans must be accountable for claims.
Claims must be falsifiable and inspectable.
Engagement must be real (responding, revising, integrating critique).
AI tools can sharpen human thinking—or amplify human sloppiness. They do not change who is responsible.
This framing is also harder for bad actors to exploit: they can generate polished text easily, but they cannot easily sustain accountable dialogue, handle cross-examination, or maintain consistency under scrutiny.
Moderation should be designed to detect lack of accountability, not the presence of assistance.
The underlying shift communities must face
We are now living in a world where:
fluent expert-shaped language is cheap
synthesis patterns are widely accessible
clarity is no longer evidence of training
coherence is no longer evidence of grounding
This is the real discontinuity.
Communities can respond by trying to rebuild scarcity through "AI-written bans," or they can evolve their epistemic defenses toward what actually matters: grounding, responsibility, traceability, falsifiability.
If epistemic communities confuse tooling with agency, or fluency with authority, they risk reinforcing the exact failure mode they seek to prevent.
A note on epistemic drift
Any community dedicated to being "less wrong" can easily slide into a community dedicated to deciding who is right and who is wrong. The difference is this: one evaluates claims. The other evaluates people.
When moderation shifts from "is this argument sound?" to "does this person pass our legitimacy tests?"—tests based on style, fluency, suspected tooling, or conformity to community aesthetics—the mission has already failed.
Epistemic hygiene demands we scrutinize what is said, not who is allowed to say it or how they assembled the words.
The moment a community begins filtering people instead of filtering arguments, it stops being an epistemic community. It becomes a social one.
Aim your safeguards at the correct target: human accountability, not production method.
Respectfully, Elin Nguyen
