Why Banning AI Buzzwords Does Not Make AI Text Read Human

Under U.S. Copyright Office guidance, copyright does not extend to purely AI-generated material, and whether human contributions amount to authorship is analyzed case by case. Amazon KDP requires authors to disclose AI-generated text even after substantial edits. Neither line is drawn at vocabulary.

John Overman's newsletter post "AI Copyright: Lesson Learned" quotes Amazon's KDP content guidelines directly: "If you used an AI-based tool to create the actual content (whether text, images, or translations), it is considered 'AI-generated,' even if you applied substantial edits afterwards." AI-assisted content, by contrast, is content the author created and only edited, refined, error-checked, brainstormed or improved with AI tools, and KDP does not require authors to disclose it. The Copyright Office report he cites adds that using AI "to assist rather than stand in for human creativity" does not affect copyright protection.

An editor who strips flagged phrases from an AI-drafted manuscript has therefore not moved it from "AI-generated" to "AI-assisted" under Amazon's definitions. Phrase removal changes the word inventory, not who created the content.

Detection accuracy adds a separate problem. The University of San Diego guide reports that neurodivergent students and students writing in English as a second language are flagged by AI detectors at higher rates than native English speakers. A Stanford HAI report, AI-Detectors Biased Against Non-Native English Writers, found that seven detectors were near-perfect on essays by U.S.-born eighth-graders but classified 61.22% of TOEFL essays by non-native English students as AI-generated. Phrase bans do nothing about that gap.

Why do AI detectors produce false positives on human writing?

One plausible reason is that neutral institutional prose sits close to the register AI models imitate. Three of the four pre-2021 Medium posts an earlier version of our detector called AI were institutional.

We collected 2,868 Medium posts that Common Crawl archived before 31 December 2020, one post per author. An earlier version of our detector called 4 of them AI (0.14%). Three were institutional prose: a US government aid programme update, a policy analysis of an Affordable Care Act bill and a corporate design case study. The fourth was a list of 100 headline-style post titles. There were no false positives in tech tutorials, data science, crypto and finance, startups, personal essays or culture writing. The current version called 2 of the 2,868.

Government writing tested the pattern. On 637 explanatory documents from US federal agencies archived before 2021, the previous version called 8 AI (1.26%), above our 0.5% cap, and plain-English copy written for international readers was the hardest group. After we added 100 documents from other agencies to training, the new version called 2 of the 637, and 0 of 511 documents from 8 agencies it had never seen. Across 9,963 verified human documents in total it called 4 AI, and on a 4,546-document pool it called none (95% upper bound 0.08%).

A banned-words list does not address what those misfires had in common, which was register rather than vocabulary. Whether stripping buzzwords pushes a draft further toward that neutral register is a plausible concern that we have not measured.

Style tells behave the same way. In one company's AI-drafted articles, a sanitizer had replaced every em dash with a spaced hyphen, and the text still carried 12.9 dash-punctuation marks per 1,000 words against 0.8 in its own pre-AI blog. Swapping the character kept the rhythm. A budget of 2 dash marks per 1,000 words and rewritten asides brought new articles to 1.69.

Our detector gives two separate readings that are never blended: origin, meaning who most likely wrote the text, and style, meaning how generic or distinctive it reads. A human writer can produce generic prose, which is exactly the case our false-positive cap exists for: at most 0.5% of human documents called AI, in every register.

Outside tools show a wide spread. The University of San Diego guide reports that Turnitin cites a false-positive rate below 1%, while a Washington Post study found 50% for one detector, though with a much smaller sample. In AI detectors rarely flag human writing, but sometimes miss AI text (Epoch AI, July 2026), Pangram and GPTZero flagged 0 of 495 human passages and Originality.ai flagged 19.

Evasion can be cheap. Cat Casey, chief growth officer at Reveal and a member of the New York State Bar AI Task Force, is quoted in the guide saying she can fool detectors 80-90% of the time by adding the single word "cheeky" to her prompt. Turnitin says its checker can miss roughly 15% of AI-generated text and that it accepts this because it does not want to flag human-written text as AI. The company frames that as a deliberate tradeoff between the two errors, and no phrase ban changes it.

What makes some article section types read AI 100% of the time while others read AI only 8%?

In our data the split followed how much the model wrote from scratch: decision frameworks read AI 100% of the time, while author bios built from a real profile read AI only 8%.

We scored 2,625 visible sections from 146 AI-drafted articles written for 13 websites in September 2026, each section separately. Decision frameworks read AI in all 16 cases, outlook charts 99% of the time, before-and-after comparisons 94% and myth-vs-fact boxes 87%. Calls to action read AI 36% of the time and human 33%; curated resource lists 42% and 21%; author bios 8% and 51%. As a control, human Medium posts cut into 50-, 100-, 200- and 300-word chunks were called AI 1.0%, 0%, 0% and 0% of the time, so short sections were not flagged just for being short.

Shared phrasing was not what separated them. Phrases shared across different websites made up 0.5% or less of every prose section, yet every generated prose section type still read AI 81-100% of the time. The detector here was trained on that pipeline's output, so it recognises the pipeline's fingerprint by construction.

Why would a decision framework read AI every time? One plausible explanation is that a fixed shape leaves the model little to draw on beyond general knowledge, while a real author profile supplies facts about one person. Our data shows where the shapes concentrate; it does not prove that mechanism.

The clearest difference inside body sections came from first-hand records, on one site. For a sports equipment manufacturer whose articles are built on its own sales and shipping records, record-heavy sections read AI 37% of the time, against 71% for the other sections of the same articles. The limit: for a payments company whose articles used general industry figures, the split was 98% against 100%.

General-purpose detectors see much less of this. Four open detectors (desklib, Fakespot, Binoculars and Fast-DetectGPT) called 0 of 223 held-out articles from our pipeline AI at a 1% false-positive threshold, while catching 13-80% of ordinary public AI text. A classifier trained on our pipeline's output separated the same articles from human writing perfectly (AUROC 1.000), so the fingerprint is learnable. Commercial detectors such as GPTZero and Pangram were not part of that test.

Newsrooms have reached a related conclusion from the policy side. A Nieman Lab analysis by Hannes Cools and Nicholas Diakopoulos, covering 21 newsroom AI guidelines published by July 2023, found approaches ranging from outright bans (Wired does not publish AI-generated text, except when that is the point of the story, or text edited by AI) to conditional rules (The Guardian requires senior editor permission). The Dutch news agency ANP describes its process as "Human>Machine>Human." The authors note that "it is often far from clear from the guidelines how these mentions of transparency will take shape."

Guidelines like these govern who approves and labels AI output. Whether a section reads generic depends on what it was built from, and that leaves one question an editor can ask of every section: does it contain information only this organization could publish?

In July 2026 we measured 816 AI-drafted articles across 24 websites, and explicit bans worked literally: banned words appeared in 0-12 of them (98-100% compliance). In a separate September test, with shared phrases down to 0.5% or less of each section, 81-100% of generated prose sections still read AI to a detector trained on that pipeline.

What do the "AI words to avoid" lists actually measure?

Shared phrasing across a fleet of articles. The lists catch wording that many AI drafts reuse, but they say little about the underlying model style that makes a passage read machine-written.

The 816-article corpus shows how visible fleet phrasing is. One stock opening phrase appeared in 74% of articles, on every site, and em dash characters appeared in 34%. You can count patterns like these and report a number, which is one plausible reason word lists became a default quality check for large pipelines. Model style cannot be counted the same way.

I think of this as the two-question test: one question about the fleet fingerprint, one about model style. A compliance audit that confirms the banned words are gone answers the first question. It does not answer the second.

Phrase lists answer the first question well. They say little about the second.

What reduced shared wording further was not a longer ban list. When each article was assigned its own sampled opening move, rhythm quota and phrasing, verbatim 8-word overlap with the rest of the corpus fell from 60 shared phrases in the control to 29, then to 18, and to 2 once per-site phrasing pools and a structure sampler were added. The channel mattered as well. The same style instruction was partly ignored when returned inside a tool response, followed almost perfectly when embedded in the writing skill, and followed best when delivered as per-section delivery notes.

Instructions can also create the tell they are meant to remove. A "humanizer technique" that mandated negative parallelism ("it's not X, it's Y") was itself one of the best-known AI tells. A rule that asked for dash asides next to a ban on em dashes produced spaced-hyphen asides instead.

Labels and wording also sit apart from how readers respond. In a Stanford HAI policy brief on a study by Isabel Gallegos, Robb Willer and colleagues, more than 1,500 U.S. participants read GPT-4o policy messages presented as written by an expert AI model, by a human policy expert, or with no authorship information. The label changed whether people thought the author was AI or human. It did not significantly change how persuasive they found the messages. The study covered four less-polarized policy topics and measured persuasion, not detection, so it supports a narrow point: a surface cue about origin did not change how readers weighed the argument.

The detector behind our section readings is a ModernBERT classifier plus a separate predictability check from a pair of open Qwen2.5 models. It scores passages of about 300 words, calls AI at 0.998 or higher and human below 0.5, and leaves everything in between not called. Its false-positive cap is at most 0.5% of human documents in every writing register, and on a 4,546-document pool of verified human writing it called none AI.

The clearest difference we found came from evidence, not wording. On one sports equipment manufacturer's site, article sections built on its own sales and shipping records read AI 37% of the time, against 71% for the other sections of the same articles. For a payments company whose articles used general industry figures, the same comparison came out at 98% against 100%. One site is not a rule, but it points at what the sections were built from rather than which words they used.

What will matter most for AI content compliance in the next 12 to 24 months?

We expect section-level style signals, disclosure rules tied to who created the content, and first-hand evidence to matter more than phrase bans. These are expectations, not measured outcomes.

The phrase-ban instinct treats detection as a vocabulary problem and disclosure as a detection problem. Neither holds up against the evidence above.

A common prediction is that detection will eventually catch everything and force universal disclosure. The evidence so far is mixed. Commercial detectors in Epoch AI's test caught plainly prompted AI text almost every time, yet style imitation still slipped past them, and the RAID benchmark (ACL 2024) found detectors easily fooled by adversarial attacks, sampling changes, repetition penalties and unseen models. We expect teams that solve the substance problem first to be less exposed whichever way detection goes.

The phrase-ban instinct is reasonable: AI generators produce repetitive wording at scale, and that wording is measurable and removable. Our bans reached 98-100% compliance. But compliance stops at the fleet fingerprint. The model style underneath stayed, and 81-100% of generated prose sections still read AI to a detector trained on that pipeline.

Prompt-level fixes have limits of their own. One experienced user in r/PromptEngineering writes that as a conversation moves out of the context window, a chatbot "will fade back to the defaults." In our pipeline, the same style instruction was followed best when delivered as per-section notes rather than returned inside a tool response.

Disclosure is a separate line again. Amazon KDP counts text an AI tool created as AI-generated even after substantial edits, so neither phrase removal nor first-hand evidence changes that label for an AI-drafted book. What first-hand evidence changes is the substance: on the one site where sections used the company's own records, those sections read AI 37% of the time against 71% for the rest.

That is what the AEO Content Engine is built for: articles grounded in each company's own records, written to read like expert human writing and structured so AI answer engines can cite them.

This article is part of our research series on how AI writes and how humans write. The overview of the whole series is How AI Writes vs How Humans Write.

To see whether a draft still reads machine-written after the phrase edits, run it through the free AI Content Detector.