Can an AI detector prove I used AI?

No. A detector measures how statistically predictable your text is, then converts that into a probability. It has no record of how the text was written. A high score means the writing resembles patterns common in AI output — which human writing can also have.

What is a normal false positive rate?

It varies enormously by tool and by who is writing. In the Liang et al. study published in Patterns, seven detectors averaged a 61.22% false positive rate on TOEFL essays written by non-native English speakers, while classifying US 8th-grade essays almost perfectly. Turnitin states its own false positive rate is under 1%; independent testing has reported higher figures on small samples.

Why do detectors flag non-native English speakers so often?

Most detectors score text on perplexity — roughly, how surprising each word is given the previous ones. People writing in a second language tend to use more common vocabulary and simpler sentence structures, which produces low perplexity. That is the same signal the detector reads as machine-generated.

Did OpenAI have a working AI detector?

It built one and withdrew it. OpenAI shut down its AI Text Classifier on 20 July 2023, citing a low rate of accuracy: the tool correctly identified about 26% of AI-written text while incorrectly flagging 9% of human writing.

Does running text through a humanizer make it undetectable?

No tool can promise that, including this one. Detectors change, and different detectors disagree with each other on the same text. Editing for clarity and voice tends to reduce false flags because it makes writing less formulaic — but nobody can guarantee a particular score from a particular tool.

Are AI detectors accurate?

Not accurately enough to accuse someone on. In peer-reviewed testing, seven detectors flagged 61% of essays by non-native English speakers as AI-written. OpenAI withdrew its own detector after it caught just 26% of AI text. A detection score measures statistical similarity to machine writing — it is not evidence of how a document was actually produced.

Last reviewed 16 August 2026 · The HumanFlow team

What the research actually found

Two findings do most of the work here, and they point the same direction.

The first is a 2023 study by Weixin Liang and colleagues at Stanford, published in Patterns. They ran 91 TOEFL essays — written by real people sitting an English proficiency exam — through seven widely used GPT detectors. The detectors misclassified an average of 61.22% of them as AI-generated. Eighty-nine of the 91 were flagged by at least one detector. Eighteen were flagged by all seven. The same detectors handled essays by US 8th-graders with near-perfect accuracy.

The second is that OpenAI, the company that built the model most of this text is accused of coming from, could not reliably detect its own output. It launched an AI Text Classifier in January 2023 and quietly killed it on 20 July that year, citing a low rate of accuracy. The published numbers: it correctly identified about 26% of AI-written text and wrongly flagged 9% of human writing.

That second fact is worth sitting with. The organisation with the deepest possible access to how its own model generates text — the weights, the training data, the sampling behaviour — concluded that detecting it after the fact did not work well enough to ship.

Why detectors flag human writing

Nearly every detector works from the same two measurements, and understanding them explains most false positives.

Perplexity is how surprising each word is, given the words before it. Language models are trained to pick likely next words, so their output tends to be unsurprising — low perplexity. Burstiness is how much sentence length and complexity vary across a passage. Human writing tends to lurch: a long winding sentence, then a short one. Model output is more even.

So a detector is not asking "did a machine write this?" It is asking "is this text predictable and uniform?" Those are not the same question, and the gap between them is where real people get hurt.

Writing that is predictable and uniform for entirely human reasons includes:

Second-language writing. A smaller working vocabulary and simpler sentence structures produce exactly the low-perplexity signature detectors punish. This is the single largest documented bias.
Technical and scientific writing, where conventional phrasing is the point and deviation is penalised by the discipline itself.
Formulaic academic structures — the five-paragraph essay taught in schools is close to an algorithm.
Heavily edited prose. Grammar tools, style checkers and careful self-editing all reduce variance. Polishing your writing can make it look more like a machine wrote it.

What a detection score actually means

A score of "85% AI" does not mean 85% of the document was machine-written, and it does not mean there is an 85% chance you cheated. It means the text scored in a range the vendor has associated with AI output, based on their model and their threshold.

Two things follow. First, the number is only meaningful relative to that vendor's calibration — which is why the same paragraph routinely gets very different scores from different tools. Second, the base rate matters enormously. If a class of 300 students submits honest work and a detector has even a 5% false positive rate, fifteen innocent people get flagged. Run it across a university and the arithmetic gets ugly fast.

What we don't know

Being straight about the limits of the evidence above, because most pages on this topic are not:

Our own benchmark protocol is now published at /research/benchmark — the method in full, with no results attached until the first run in Q4 2026. The Liang study tested detectors as they existed in 2023. Detection has moved since, and some vendors have specifically worked on the ESL bias. The direction of the finding is well established; the current magnitude for any specific tool is not.
Vendor-published accuracy figures are marketing, ours included. Almost none are independently reproducible, because the test corpora are not released.
There is no public, maintained benchmark that tests detectors against provably human, pre-2022 text across writer demographics. That gap is why this page leans on a three-year-old paper — it remains among the most rigorous work available.

Before arguing about the number

Establish what it measures. An asterisk is Turnitin withholding a figure it does not trust, 20% is where it starts displaying one rather than a limit, and a GPTZero probability is confidence in its own verdict rather than a share of your document — what a score actually means.

What the tool will not read

Before reading a score, check there could have been one. Turnitin’s AI report needs at least 300 words of prose, caps at 30,000, covers English, Spanish and Japanese only, and does not accept slides — what that means for PDFs and for PowerPoint. A missing score is frequently a file limit rather than a finding.

Tool by tool

“Are AI detectors accurate” has no single answer, because the tools differ more than their marketing suggests and each publishes a figure measured under conditions it chose. We reviewed the seven you are most likely to meet, and in every case the interesting number is the gap between what the vendor measured and what independent testing found:

Turnitin — the one most universities actually run, and the only vendor to publish a false-positive target
Copyleaks — best of seven in peer-reviewed testing, and the highest false-accusation rate in the same study
GPTZero — the tool an individual instructor is most likely to reach for
Originality.ai — built for publishers, and until 2024 its own CEO advised against academic use
ZeroGPT — free, and its false-positive rate ran from 3.7% to 39.4% across the same corpus depending only on subject
Scribbr — free and decent, and never independently tested by anyone
Compilatio — 94–99% on its own test, under 78% on the peer-reviewed one
SafeAssign — does not detect AI at all, and its maker published the testing behind that decision

Two of those are free to use, and several others run a free tier that is really a demo. Because none of them publishes enough methodology to rank on accuracy, the only honest comparison is of what each one lets you check — the free detectors ranked by word limits and transparency sets out the caps, the sign-up requirements and what each readout actually shows.

If you have been wrongly flagged

The practical position is stronger than most people realise, because the burden of proof does not sit where an accusation implies. A detector score is one piece of weak statistical evidence, and every major vendor says in its own documentation that scores should not be used as the sole basis for an accusation.

Draft history is the most effective counter-evidence there is: Google Docs version history, Word's AutoSave revisions, commit timestamps, notes and outlines. If you are writing anything that could later be questioned, work somewhere that keeps a revision trail. It costs nothing and it settles the argument.

More on why text gets flagged and what to do about it.

Where HumanFlow sits

HumanFlow includes a detector, and everything above applies to it too. It gives you a score, how confident it is and the signals behind it — and on a paid plan a sentence-level read of which passages look statistically machine-like, which is useful for spotting the parts of a draft that read as flat or formulaic. It is not proof of anything, and we do not claim it can tell you what another tool will say.

The humanizer exists to make writing sound more like a person wrote it. That often reduces false flags, because varied, specific prose is exactly what detectors read as human. We will not promise you a score, from us or from anyone else.

One detector has materially better independent evidence than the rest of the category, and this page would be dishonest without saying so: what Pangram claims and what independent work found.

The two numbers an accuracy figure averages away are precision and recall — worked through on 100 essays, where 94% accuracy turns out to mean two students wrongly accused and four AI essays missed. And any rate from a small test needs a confidence interval: one error in twenty documents is compatible with a true rate anywhere from 0.9% to 23.6%.

Two definitions that make these figures readable, each with a worked example: false negative — the error nobody appeals, so nobody counts — and the fuller picture in our review of the accuracy research.

Worth knowing if you are choosing between tools: what the detectors actually charge is a comparison we could build, because pricing is published. Not one of those pricing pages states an accuracy rate, a false-positive rate or a precision figure — every percentage on them is a discount — which is why no page on this site ranks detectors by how often they are right.

Common questions

Can an AI detector prove I used AI?: No. A detector measures how statistically predictable your text is, then converts that into a probability. It has no record of how the text was written. A high score means the writing resembles patterns common in AI output — which human writing can also have.
What is a normal false positive rate?: It varies enormously by tool and by who is writing. In the Liang et al. study published in Patterns, seven detectors averaged a 61.22% false positive rate on TOEFL essays written by non-native English speakers, while classifying US 8th-grade essays almost perfectly. Turnitin states its own false positive rate is under 1%; independent testing has reported higher figures on small samples.
Why do detectors flag non-native English speakers so often?: Most detectors score text on perplexity — roughly, how surprising each word is given the previous ones. People writing in a second language tend to use more common vocabulary and simpler sentence structures, which produces low perplexity. That is the same signal the detector reads as machine-generated.
Did OpenAI have a working AI detector?: It built one and withdrew it. OpenAI shut down its AI Text Classifier on 20 July 2023, citing a low rate of accuracy: the tool correctly identified about 26% of AI-written text while incorrectly flagging 9% of human writing.
Does running text through a humanizer make it undetectable?: No tool can promise that, including this one. Detectors change, and different detectors disagree with each other on the same text. Editing for clarity and voice tends to reduce false flags because it makes writing less formulaic — but nobody can guarantee a particular score from a particular tool.

Sources

1.GPT detectors are biased against non-native English writers — Liang, Yuksekgonul, Mao, Wu & Zou — Patterns (Cell Press), 2023
2.OpenAI scuttles AI-written text detector over 'low rate of accuracy' — TechCrunch, 2023
3.OpenAI Shuts Down Flawed AI Detector — Search Engine Journal, 2023
4.Comparing Turnitin AI to OpenAI's own text classifier — Turnitin, 2023
5.AI detection tools falsely accuse international students of cheating — The Markup, 2023

We update this page when significant new research is published. The review date is at the top, from lib/reviewed.ts — a second one down here quoted a hardcoded constant and drifted seven weeks behind it.