Why AI Detectors Flag Text: 28 Reasons Explained
When a detector marks your writing, it names the pattern it reacted to. This page defines every one of those reasons: what the signal means, why a classifier treats it as machine-like, what it looks like in a sentence, and what to change. A flagged pattern is not proof of anything — human writing carries these patterns too, which is exactly why they are worth understanding.
Read this first
- A reason is a pattern, not a verdict. Each item below describes a measurable property of the text. None of them can show who wrote a document.
- Single signals are weak; combinations are strong. Detectors weigh dozens of these together. One flagged pattern in an otherwise varied text moves the score very little.
- Careful human writing sets off several of these. Formal register, heavy editing, a rigid outline and a house style all push a text toward the machine side of every measure in group 5.
- Fixing the pattern is not the same as hiding it. The changes suggested here make writing more specific and more varied. That is why they lower a score: the text carries more information, not less.
What a Detector Actually Measures
A detector does not read for meaning. It converts your text into numbers and compares those numbers with what a language model would have produced. Three families of measurement do almost all of the work, and every reason on this page belongs to one of them.
1. Predictability, sentence by sentence
The text is run through a language model, which records how surprised it is by each word. Low surprise across a whole document means the wording followed the most likely path. This is where vocabulary and phrasing signals come from.
2. Variation, across the document
Sentence lengths, clause shapes and paragraph sizes are measured as a distribution. Human writing is uneven. Generated writing clusters around one comfortable shape, and the flatness of that distribution is measurable.
3. Style markers that can be counted
Contractions, hedges, first-person pronouns, transition words, list density, punctuation range, named entities and numerals. Each one is a count, and each count has a different distribution in human and machine text.
How surprising the wording is to a language model. Low perplexity means predictable text. Groups 1 and 4 on this page are mostly perplexity signals.
How much sentence length and rhythm vary. Human drafts spike and dip. Group 2 collects the signals built on this measure.
Countable habits: hedges, connectors, pronouns, punctuation, numerals. Groups 3 and 5 are built almost entirely from these.
All 28 Reasons, Defined
Search by name, or filter by what the signal measures. Every definition carries its own link, so you can point a colleague, a student or a marker straight at it.
Try one word, such as sentence, transition, evidence or template.
Vocabulary and word choice
These signals come from the words themselves. A language model picks each next word from a probability distribution, and the safe middle of that distribution is narrow. Detectors measure how far your word choices sit from the words a model would have picked.
The text reuses a small set of words instead of drawing on the wider vocabulary a topic normally invites.
Detectors compute type-token ratio and related lexical-diversity measures: how many distinct words appear per hundred words of text. Human writers reach for synonyms, near-synonyms and side references almost without noticing, so their diversity score fluctuates from paragraph to paragraph. A model optimising for the most probable next word tends to settle on one preferred term and keep it. Low diversity on its own proves nothing, but combined with the other signals here it moves the score.
The system improves efficiency. Improved efficiency helps the process. The process becomes more efficient as a result.
The setup runs leaner. That saves about forty minutes a shift, which is really the whole point of the rebuild.
Fix: Name the same thing in different ways, and let some sentences drop the keyword entirely.
One term, often the topic word, appears far more often than a human writer would repeat it.
This is the concentrated form of low lexical variation, and it is the easiest of the vocabulary signals to see without a tool. Models keep the topic word in play because repeating it keeps the next prediction on track. Writers do the same thing on purpose when they optimise for a search engine, which is why keyword-stuffed human text often trips detectors too. Detectors look at the frequency of the top term against the length of the text and against how often the term carries new information.
Time management is essential. Good time management improves results, and time management skills can be learned through practice in time management.
Time management is essential. Get it right and the rest of the week stops fighting you; get it wrong and no amount of effort catches up.
Fix: Count your topic word. If it appears in most sentences, cut it from half of them and let pronouns and context carry the meaning.
Each word is the one most readers would guess from the words before it.
This signal is close to the mathematical core of AI detection. Detectors run the text through a language model and record how surprised the model is by each token, a measure called perplexity. Low perplexity means the text sits where the model expected it. Human writing carries small deviations from the expected path: an odd adjective, a blunt verb, a word that belongs to a different register. Text with no deviations reads smoothly and scores as machine-like.
It is important to note that this approach offers a robust solution and plays a crucial role in delivering meaningful results.
This approach works, and it is cheap, which matters more than either of those words suggests.
Fix: Find the sentences you wrote fastest. Those are usually the most predictable ones. Replace the safest word in each with a specific one.
Whole phrases, not just single words, follow the most expected pattern.
Perplexity measured across longer spans catches something single-word measures miss: fixed multi-word blocks. Phrases such as in today's fast-paced world or plays a vital role in are stored as units by the model and reproduced as units. A detector that sees several such blocks in one text has strong evidence, because a human writer usually breaks at least one of them by accident. This signal is also the reason paraphrasing tools often fail to lower a score: they swap single words and leave the block structure intact.
In today's rapidly evolving digital landscape, it is more important than ever to leverage cutting-edge solutions.
The tooling changed twice last year. Whatever you standardise on now, budget for replacing it.
Fix: Read the text and mark every phrase you have seen before in someone else's writing. Rewrite those spans from scratch, not word by word.
The wording would fit almost any topic, because it says nothing that only this topic allows.
A model asked for a paragraph about a subject it knows little about will produce sentences that are true of most subjects. Detectors pick this up as a combination of low information density and high phrase predictability: many words, few claims that could be checked. It is also the signal readers notice first, which is why generic phrasing costs credibility even when no detector is involved.
This topic is a complex and multifaceted issue with a wide range of important implications for various stakeholders.
Two groups care about this: the people who maintain the index, and the people who get paged when it breaks.
Fix: Test each sentence with one question: could this sentence appear in an article about a different subject? If yes, replace it with something only your subject makes true.
Sentence structure and rhythm
These signals ignore what the text says and measure how it is built. Sentence length, clause order and punctuation form a statistical fingerprint. Human writing is uneven; generated writing tends toward one comfortable shape and stays there.
Nearly every sentence runs to about the same number of words.
This is burstiness, the second measure most detectors report alongside perplexity. A detector takes the length of every sentence, then computes the variation around the mean. Human writers produce a jagged line: a twenty-eight-word sentence, then a four-word one, then something in between. Generated text produces a flat line, usually in the fifteen to twenty-five word band, because each sentence is built by the same process from the same kind of prompt. A very low variation is one of the strongest single indicators in the whole list.
The project began in March with three engineers. The team delivered the first prototype in June that year. The results exceeded the original targets set in the plan.
The project began in March with three engineers, one of whom left in April. We shipped in June. Barely.
Fix: Put a sentence of five words or fewer next to your longest sentence. Do this two or three times per page.
Sentences repeat the same grammatical construction one after another.
Detectors parse each sentence into its parts and compare the resulting shapes. When many sentences share a template, subject then verb then object then trailing clause, the shape sequence becomes compressible, and compressibility is what these models measure. Humans vary construction unconsciously: they front a clause, invert for emphasis, start with a conjunction, or break a sentence in half. Generated text repeats the construction that had the highest probability.
The tool analyses the text and returns a score. The system reviews the input and produces a report. The model examines the content and delivers a verdict.
You paste the text, the tool scores it. What comes back is a report, though report is a generous word for two numbers and a colour.
Fix: Read three consecutive sentences aloud. If they open the same way, rebuild the middle one around a different clause.
The stress pattern and the pauses fall in the same places sentence after sentence.
Rhythm is length and punctuation together: where the commas land, how many beats run before the first pause, whether the sentence ends on a stressed word. Detectors capture this through the distribution of clause lengths inside sentences rather than the sentences themselves. Two sentences can differ in word count and still share a rhythm, which is why this signal fires on text that already passes a burstiness check. Read the text aloud and the sameness is audible before any tool measures it.
Working carefully, the team reviewed the data, checking each result. Moving quickly, the group revised the plan, adjusting each step.
The team went through the data line by line. Slow work. Then somebody noticed the timestamps were in two different zones and the plan changed.
Fix: Move a comma. Split one sentence at its comma and let the second half stand alone.
Sentences close on the word or phrase a reader would guess halfway through.
Endings carry more weight than any other position in a sentence, and models handle them conservatively. A detector measuring per-token surprise finds the lowest values on the final tokens of generated sentences, because by then the sentence has committed to a path. Common endings include phrases such as in the long run, for years to come or and much more. Human sentences end on the specific thing being discussed, or stop early, or run past the expected point.
Careful planning helps teams achieve their goals and deliver better results in the long run.
Careful planning helps, up to the point where the plan meets a customer.
Fix: Cut the last four words of a sentence and see whether it still works. Often it works better.
Every paragraph has the same number of sentences and the same internal shape.
This is the paragraph-level version of uniform sentence length. Generated text tends to produce blocks of three to five sentences, each block opening with a claim, adding two supports and closing with a summary line. A detector measures paragraph length variance and the position of topic sentences. Human documents are lopsided: a one-sentence paragraph, then a long one, then a list, because the material dictates the shape rather than the format.
Three paragraphs, each four sentences long, each opening with a claim and closing with a restatement of that claim.
A three-line paragraph, then a single sentence that carries the point, then a longer paragraph that has to explain the exception.
Fix: Let one paragraph be a single sentence. Let another run long because the argument needs it.
Punctuation is applied with a regularity, or a narrowness, that human drafts rarely show.
Two opposite patterns fire this signal. The first is a very small punctuation set: commas and full stops only, no dashes, semicolons, brackets, question marks or ellipses across a long text. The second is mechanical regularity, such as the serial comma applied without exception, or an em dash in the same position in many paragraphs. Detectors also watch character-level details, including the use of typographic quotation marks and spaced en dashes, which correlate with generated output. Human punctuation is inconsistent because it follows the voice rather than a rule.
The report was clear, detailed, and accurate. The team was fast, focused, and reliable. The result was solid, useful, and complete.
The report was clear enough — detailed, mostly accurate. The team moved fast. (Whether that was good is another question.)
Fix: Use one question mark, one dash and one bracketed aside on each page, where the meaning invites them.
Flow, transitions and layout
These signals sit between sentences and between paragraphs. Generated text connects its parts with explicit signposts, because the model was trained to make the join visible. Human writing more often lets the sequence of ideas carry the connection.
Connectors such as furthermore, moreover and additionally open a large share of the sentences.
Transition words are cheap for a model: they are high-probability openers that fit almost any continuation, and instruction-tuned models were rewarded for producing text that reads as organised. Detectors count connectors as a share of sentence openings. Published human prose runs at roughly one connector every eight to twelve sentences; generated prose often runs at one in three. The words themselves are not wrong, and formal academic writing legitimately uses more of them, which is why this signal is weighted rather than decisive.
Furthermore, the results were positive. Moreover, the team met the deadline. Additionally, costs stayed within budget. In conclusion, the project succeeded.
The results were positive and the team hit the deadline. Costs stayed inside the budget, which nobody expected.
Fix: Delete every connector, then read the text. Put back only the ones whose absence changed the meaning.
The same connector, or the same small set of connectors, is reused throughout the text.
This differs from the previous signal in what it measures: not how many connectors appear, but how few distinct ones. A model that has settled on however will use however for every contrast in the document, while a human writer alternates between but, though, yet, still, and a full stop followed by a fresh start. Detectors track the frequency of each connector and flag the case where one term dominates. The pattern survives paraphrasing, because paraphrasers usually treat connectors as fixed.
However, the cost was high. However, the team continued. However, the outcome justified the spend.
The cost was high. The team kept going anyway, and in the end that was the right call.
Fix: List every connector you used. If one appears more than twice per page, replace all but one instance.
Paragraphs begin with the same kind of lead-in, often a signpost phrase or a restatement of the heading.
Openings are where a model most reliably falls back on template language: One of the key aspects, It is worth noting that, When it comes to. Detectors examine the first few tokens of each paragraph as a separate sequence, because that position concentrates the signal. The pattern also appears in text written by humans against a rigid outline, which is a common source of false positives in school and workplace documents.
One of the key aspects to consider is cost. Another important factor to keep in mind is time. It is also worth noting that quality matters.
Cost decides this one. Time matters less than people assume, because the slow part is approval, not build.
Fix: Start a paragraph with the specific noun it is about, not with a phrase announcing that a point is coming.
Every sentence follows from the previous one with no digression, no gap and no change of pace.
Human drafts contain friction: an aside, a qualification added after the fact, a sentence that arrives out of order because the writer thought of it late. Detectors approximate this through semantic-distance measures between consecutive sentences. Generated text keeps that distance small and constant, producing a curve with no spikes. The result reads well, which is the difficulty with this signal: the text is not worse, it is only more even than a person's first draft.
The team planned the work. The plan was then approved. The approved plan guided the build. The build followed the schedule.
The team planned the work, and approval took three weeks, which is a story of its own. By the time the build started the schedule was fiction.
Fix: Add one sentence that steps sideways: an aside, an exception, or a detail that only matters to someone who has done this work.
Content that would normally be prose is broken into bullet points, numbered steps or bold labels.
Chat models produce lists by default, because the format was rewarded during training as easy to read. Detectors treat heavy list density as a structural signal, and they also notice the pattern inside list items: parallel construction, similar length, a bold label followed by a colon and one sentence. Lists are correct in documentation and in procedures, so this signal carries little weight alone. It becomes evidence when the surrounding prose shows the other structural patterns.
Key benefits: Improved efficiency: the system runs faster. Reduced cost: fewer resources are needed. Better quality: results are more accurate.
It runs faster and costs less to run. Quality is harder to judge, but nothing has broken since April.
Fix: Convert one list back into a paragraph. Keep a list only where the items are genuinely parallel, such as steps or parameters.
Repetition and template shapes
These signals concern what the text does with its own content: how often it returns to a point it has already made, and whether its opening and closing follow a stock form. This is the group most visible to a reader, and the easiest to correct.
The same point is made more than once in different words, without adding anything the second time.
Models pad. When a target length exceeds the material available, the highest-probability continuation is a restatement of what came before, dressed in different vocabulary. Detectors measure this with semantic similarity between sentences and paragraphs: two spans that differ in wording but sit close together in meaning space. Human writers repeat too, but usually to build an argument, so the repeat carries new information. A repeat that carries none is the pattern that fires.
Regular exercise improves health. Working out on a regular basis has a positive effect on your wellbeing. Physical activity done consistently benefits the body.
Regular exercise improves health, though the effect size in the trials is smaller than the headlines suggest.
Fix: Find pairs of sentences that make the same claim. Keep the more specific one and delete the other.
The text keeps summarising itself, at the end of sections and often at the end of paragraphs.
Summary is the default closing move of an instruction-tuned model, and it appears at every level of the document, not only at the end. Detectors notice summary language, in summary, overall, to sum up, and they notice the semantic pattern: a sentence whose content is fully contained in the sentences before it. Recurring summary blocks are also a strong stylistic marker, because most human writing summarises once, if at all.
In summary, the three factors above all contribute to the outcome, and together they demonstrate the importance of the approach discussed in this section.
Those three factors are the ones that moved the number. The rest we measured and dropped.
Fix: Delete every closing summary except the one at the end of the document, and check whether that one is needed either.
A sentence repeats the question, the heading or the previous claim before it says anything new.
Restatement is how a model uses the prompt to steady its own output: it echoes the input before continuing. The result is a recognisable opening move, such as When it comes to the question of whether X, there are several things to consider. Detectors catch this as near-duplicate content between the heading and the first sentence beneath it, and as sentences with high overlap and low new information. Human writers usually assume the reader remembers the heading.
When considering the question of whether remote work improves productivity, it is important to examine whether remote work improves productivity.
Remote work improved productivity for our support team and hurt it for onboarding. Same policy, opposite result.
Fix: Cut the first sentence of each section. If the section still makes sense, the sentence was a restatement.
The opening follows a stock form: broad context, a claim that the topic matters, a promise of what follows.
Openings are the most templated part of generated text, because the model has the least information at that point and falls back on the safest structure it learned. Detectors match against known opening patterns, including In today's world, With the rise of, and It is no secret that, and they weight the first fifty tokens more heavily than the rest. This signal is one of the strongest in short texts, where the introduction is a large share of the total.
In today's fast-paced digital world, technology plays an increasingly important role in our daily lives. This article explores the key aspects of this important topic.
Our build times went from four minutes to nineteen over eight months. Nobody noticed until a release slipped.
Fix: Delete the first paragraph. Start at the first sentence that states a fact only you could state.
The closing restates the points already made and ends on a general statement about the future or the importance of the topic.
The mirror image of the templated introduction, and it fires on the same kind of match. Typical forms include In conclusion, By understanding these factors, and As we move forward. Detectors weight the final tokens heavily for the same reason they weight the opening: it is a position where models are most predictable and human writers are most varied. Human documents often stop when the material runs out, without a closing move at all.
In conclusion, by understanding these key factors, individuals and organisations alike can make more informed decisions as they navigate this evolving landscape.
We are keeping the current setup until the next audit. If the numbers hold, we will move the rest of the fleet over in March.
Fix: End on the last piece of new information, a decision, a number, or an open question. Delete the paragraph after it.
Tone, stance and evidence
These signals are about position: whether anyone is present in the text, whether the text commits to anything, and whether its claims are anchored to checkable detail. This group produces the most false positives, because formal genres ask writers to remove exactly these traces.
The register stays formal throughout: no contractions, long Latinate words, and no shift in tone.
Detectors track register markers, and the absence of contractions is the single easiest one to count. Human writing mixes registers, dropping into plain words for the important sentence and back into formal language for the qualification. Generated text holds one register from start to finish because nothing in its process causes a shift. Note the risk of a false positive here: legal, academic and medical writing is formal by requirement, not by machine.
It is imperative that stakeholders endeavour to facilitate the implementation of the aforementioned methodology in a timely manner.
We need this shipped by Friday. If the method does not hold up under load, say so now rather than after the release.
Fix: Use contractions where you would use them in speech, and replace two Latinate words per paragraph with plain ones.
The text has no rough edges: no false starts, no awkward joins, no sentence that was clearly revised.
Detectors do not measure polish directly. They measure its consequences: flat perplexity, low burstiness, uniform register, and the absence of the small irregularities that survive human revision. A well-edited human document can score high here, which is the most common cause of a false accusation. A first draft that has been read once by its author almost never scores high, because something always survives.
Each paragraph is balanced, every transition is smooth, and no sentence is longer or shorter than it needs to be.
One paragraph runs too long because the exception took three sentences to explain and none of them would come out.
Fix: Keep one sentence that you would normally smooth out. Uneven and correct beats even and generic.
Nobody is present in the text: no first person, no stated preference, no sign of who wrote it.
Detectors count first-person pronouns, opinion verbs such as think and doubt, direct address, and markers of stance. Text with none of these reads as authorless, which is the default mode of a model answering a general question. The signal is easy to game, so it carries moderate weight on its own, and it produces false positives on genres that forbid the first person, including many scientific papers and news reports.
There are several advantages to this approach, and it is generally considered effective by many practitioners in the field.
I was against this approach for a year. The thing that changed my mind was the support queue.
Fix: Say who did what. Where the genre allows it, state one opinion and take responsibility for it.
Claims arrive without numbers, names, dates, places or any detail that could be checked.
This is information density, and detectors approximate it by counting named entities, numerals and dates against the length of the text. A model writing about a subject it has no direct access to produces true but unanchored statements, because inventing a specific detail risks a factual error. Human writing about lived work is dense with specifics, and those specifics are unpredictable, which raises perplexity at the same time.
The study found that the intervention had a significant positive effect on participants over a period of time.
The 2023 trial ran for eleven weeks with 340 participants, and the effect held only in the group that started before week three.
Fix: Add one checkable detail per paragraph: a number, a date, a name, a place, or a version.
Every position is given equal weight and the text never lands on one of them.
Balance is trained behaviour. Models are tuned to present multiple sides and avoid taking a position, so the arguments arrive in matched pairs with a neutral closing. Detectors see the structural trace of this: alternating stance markers, symmetric paragraph lengths, and a closing sentence that resolves nothing. Human argument is usually lopsided, because the writer has a view and the counter-argument gets the shorter treatment.
On the one hand, remote work offers flexibility. On the other hand, it presents challenges. Ultimately, the right approach depends on individual circumstances.
Remote work suits our support team and does not suit onboarding. We kept it for one and dropped it for the other, and the argument took four months.
Fix: State your conclusion in the first paragraph, then give the counter-argument the space it actually deserves.
Hedges soften nearly every claim: may, might, generally, often, in some cases.
Hedging is how a model manages uncertainty it cannot resolve, and safety tuning increases it further. Detectors count hedge terms per hundred words and watch for stacked hedges in a single clause, which is rare in human writing. Careful academic prose hedges too, so density matters more than presence: two hedges in a sentence is the pattern that separates the two.
This may potentially help to somewhat improve outcomes in certain cases, although results can vary depending on a number of factors.
This helped in three of our four teams. The fourth had a different bottleneck, so the change did nothing there.
Fix: Allow yourself one hedge per paragraph. Where you cannot support a claim without two, state the limit instead of softening the claim.
Evidence is referred to but never identified: studies show, experts agree, research suggests.
A model that cannot cite a real source produces the shape of a citation without its content. Detectors match these attribution phrases and check whether a named source, date or figure follows within the sentence. The pattern is also the one most likely to be checked by a reader, since an unnamed study is unverifiable by definition. In academic submissions this signal often appears alongside fabricated references, which is a separate and more serious problem.
Studies have shown that this method is effective, and experts agree it is becoming increasingly important across the industry.
Gehring and Paassen tested eight detectors on 750 essays in 2025; the weakest caught 18.8 per cent of the AI texts.
Fix: Name the source, or delete the claim. If you cannot name it, write what you observed yourself instead.
Your Text Was Flagged. Now What?
The reasons above tell you which patterns a tool reacted to. They do not tell you whether the verdict is right. Work through these four steps in order.
Most tools report a probability, not a finding. A score of 70% is a model's estimate that text with these properties tends to be machine-written. It is not a measurement of your document's history, and no vendor's documentation claims otherwise.
Open each reason the tool listed and compare its definition with your document. If it says uniform sentence length, count your sentences. If the pattern is not there, the flag is weak, and you can say so with evidence rather than with a denial.
Every fix on this page adds information or variation: a number, a name, a shorter sentence, an opinion you are willing to defend. Text edited this way scores lower because it carries more of you in it. Text edited only to defeat a measure usually reads worse and still fails a different measure.
Version history, draft files, comments, search history and notes are worth more than any detector output. If a false flag has consequences for you, that record is the answer to it. Ask which specific passages were flagged and compare them with your drafts.
The honest limits of this page
These definitions describe the signals that AI detectors are built on, in public terms. No vendor, including us, publishes the exact weights inside its classifier, and those weights change with every model update.
- Names differ between tools. One vendor's low lexical unpredictability is another's limited vocabulary variation. The measurement underneath is close to the same.
- Weights are hidden and unstable. A signal that dominates one tool's score barely registers in another's. Our benchmark of eight tools showed detection rates from 18.8% to 96.7% on identical texts.
- Human writing triggers these patterns. Non-native writers, formulaic genres and heavily edited documents all show several signals from group 5 at once, which is the main source of false accusations.
- No detector output is evidence of authorship. It is a probability estimate produced by a statistical model, and it cannot observe who typed the words.
AI Detection Reasons: FAQ
Can my text be flagged when I wrote every word myself?
Yes. Every signal on this page occurs in human writing, and several genres produce them by design. Academic prose avoids the first person and hedges heavily. Corporate reports follow a fixed structure. Search-optimised articles repeat a keyword on purpose. Writers working in a second language often use a smaller, safer vocabulary, which is why non-native writers are flagged more often in published studies.
A detector cannot see your process. It sees the properties of the finished text, and a careful writer following a template can produce properties close to a model's. That is a real limitation of the technology, not a fault in your work.
Which of these reasons carries the most weight?
Across published research and our own testing, two measures do most of the separating: predictability of the wording, and variation in sentence length and rhythm. In this list that means predictable word choices, high phrase predictability, uniform sentence length and similar sentence rhythms.
Template signals come next, because they match against known patterns and rarely appear by accident: template-like introduction and template-like conclusion. The tone and evidence signals in group 5 are the weakest individually and the most likely to produce a false positive.
If I fix all 28 patterns, will my text pass every detector?
No, and anyone promising that is selling something. Detectors are retrained, thresholds move, and two tools disagree on the same text often enough that a pass on one says little about the next. In our benchmark, any two of eight detectors agreed on only 62.3% of AI texts.
What the fixes do reliably is make the writing more specific and more varied. That lowers the score on most tools because the text genuinely carries more information. It also makes the document better, which is the part that survives the next model update.
Why do two detectors give different reasons for the same text?
Because they measure different things and name them differently. One tool reports at the level of the whole document, another sentence by sentence. One counts transition words explicitly, another folds that count into a general predictability score and never mentions it.
The reason labels are a readable summary of a numeric decision, and each vendor writes its own summary. Treat the reason as a pointer to the passage, then judge the passage yourself.
How do I link to a single definition on this page?
Every definition has a Copy link button next to its heading. It puts the full address of that definition on your clipboard, for example cleverhumanizer.ai/ai-detection-reasons#uniform-sentence-length. Opening that address scrolls straight to the definition and highlights it.
The definition headings are also ordinary links, so you can right-click one and copy the address the usual way.
Is a detector result enough to accuse someone of using AI?
No. A detector estimates the probability that text resembles model output. It does not observe who wrote it, and an accused person cannot prove authorship after the fact, which makes a false positive uniquely hard to recover from.
Use a score as one input beside draft history, version timestamps, an in-class writing sample and a conversation about the work. Several universities have switched off detector integrations rather than manage the risk of a wrong accusation.
Clever AI Detector highlights the passages behind the score and names the reasons for each one. Free, no account, 10,000 words per check, unlimited checks.