AI Detect Flagged Your Writing: How to Get the Rate Down

Your text was flagged by an ai detect tool and you need the rate down. Start with the uncomfortable part: a detector score is a probability estimate built from surface patterns, not proof you used a model. It can be wrong. It is also usually pointing at something real — flat rhythm, uniform sentence length, generic phrasing — and that is fixable without inventing a single new idea.

What the score actually measures

Detectors do not read your intent. They measure statistical properties of the text and compare them against what human and machine writing tend to look like.

Two properties come up constantly. Perplexity is how predictable each word is given the words before it — machine output tends to sit in the predictable band. Burstiness is variation in sentence length and structure across a passage. Human writing swings: a long, clause-stacked sentence, then four words. Machine output drifts toward the middle and stays there.

That is why the same paragraph can score differently on two tools. Each one weights those signals differently, uses a different threshold, and was trained on different data. If you want the mechanics in more depth, our breakdown of how AI detection works walks through the signals one by one.

We ran our own detector over two corpora on 2026-09-28 (local ensemble, lite tier, 30% flag threshold). A set of 709 hand-written DeAIze guides scored a mean AI probability of 26%, median 25%, range 0-69%, with 239 of 709 crossing the flag line. Twelve unedited model passages, generated from fixed prompts and never touched, scored a mean of 33% and a median of 34%.

Read those two numbers together, because they are the most useful thing on this page. The gap between ordinary human writing and raw machine output is narrower than most people assume, and a chunk of genuinely human text sits above the threshold. That is the false-positive problem in one line, and it is why a flag is a starting point for editing, not a verdict.

Why your flagged sentences are the ones that look generic

Here is the part that makes the work tractable. Detection at sentence level is far more useful than a single document score, because it tells you where the signal lives. In most flagged drafts, the problem is not spread evenly. It clusters.

The usual suspects:

• Uniform sentence length. Five sentences in a row landing between 18 and 24 words.
• Transition scaffolding. “Moreover”, “Furthermore”, “Additionally” opening consecutive paragraphs.
• Hedged, abstract claims. “This approach offers several significant benefits” instead of naming one benefit.
• Symmetrical structure. Every paragraph with a topic sentence, three supports, and a summary.
• No specifics. No names, no dates, no numbers, no small awkward details that only a person in the situation would include.

None of those are crimes. They are just what happens when text is produced by averaging. Fix the pattern and the score usually follows, because the underlying statistics change.

If you want to see which of your own sentences carry the signal before you start editing, run the draft through the sentence-level AI detector first — it highlights each line by AI probability so you can fix the flagged ones and leave the rest alone.

The fix, in the order that works

Do not rewrite from the top. Work on flagged sentences only, and in this sequence.

1. Cut the transitions first. Delete “Moreover”, “Furthermore”, “In addition” at the start of sentences. Most of them are doing nothing. Where a link is genuinely needed, use a shorter one or restructure so the connection is implied.
2. Break the rhythm. Take any run of three sentences of similar length and make one of them much shorter. Not choppy for its own sake — find the sentence that lands hardest and let it stand alone.
3. Replace abstract claims with a specific. “Improved efficiency” becomes the actual thing that got faster. If you do not have the specific, you may not have the claim.
4. Add one detail only you would know. A date, a place, a name, a number from your own work. This is the single strongest human signal and it cannot be faked by a rewriter.
5. Read it aloud. Anywhere you stumble or run out of breath is a place the rhythm went flat.
6. Re-score. Check whether the flagged sentences actually moved. If one did not, the problem is usually that it is still too abstract.

A worked example

Illustration, not a real submission.

Before: “Moreover, the implementation of automated scheduling systems offers numerous advantages for organisations seeking to optimise their resource allocation processes.”

After: “We moved the rota into the scheduling tool in March. It cut the Thursday admin block from two hours to about twenty minutes.”

The second version is not better because it is casual. It is better because it has a date, a named day, a before and an after, and a sentence that ends in four words. The first version could have been written by anyone about anything, which is exactly the statistical profile a detector reacts to.

Where humanizing tools fit, and where they do not

You have two options once you know which sentences are flagged.

Approach	What it changes	Where it fails
Manual edit (the steps above)	Rhythm, specificity, transitions — and you keep full control of meaning	Slow on long documents; easy to miss patterns you cannot see in your own writing
Paraphrase tool	Word choice and sentence shape	Often preserves the flat rhythm; can drift meaning; frequently re-flags
Closed-loop humanizer (e.g. DeAIze)	Rewrites only the flagged sentences, keeps a rewrite only when semantic similarity confirms the meaning survived, then re-scores	Depends on the detector’s sentence flags being right in the first place

The honest limitation of any rewrite tool: if the flagged sentence is flagged because it is vague, a rewriter can only make it vague in a different way. Specificity has to come from you. That is why the manual steps above are not optional even if you use a tool.

DeAIze reports an average AI-score reduction of 60%+ after humanizing, with a best case of 71%. Those are the product’s own predicted scores, not an official verdict from any third-party detector. On the example paragraph used on the site, a 92% AI score becomes 4%. Treat those as an indication of what the loop does, not a promise about your specific document — no tool can promise a score on a detector it does not control.

When this is the wrong choice

Some readers should stop here and not buy anything.

If you did not write the underlying work, no amount of rewriting makes it yours. Humanizing a draft you did not produce and submitting it as your own is misrepresentation, and detector scores are not the thing that catches it — a supervisor asking you one question about your method is. The ideas, data and conclusions have to be yours.

If your institution has a clear disclosure policy, read it before you edit anything. Some allow AI assistance with a declaration; some prohibit it outright. Editing to hide a use that your policy requires you to declare is a different problem from a false positive, and the fix is disclosure, not rewriting. Our guide to academic integrity policies and what counts as misrepresentation covers where that line usually sits.

If you are writing in a second language, be aware that detectors are known to over-flag non-native English writing, because the patterns that read as “machine” overlap with the patterns of careful, formally learned English. Our piece on why AI detectors flag non-native English writers is worth reading before you spend money on a fix for a problem you may not have.

If your text is under about 100 words, no score is worth acting on. There is not enough signal.

What to do with the result

Re-score after editing and compare. If the flagged sentences dropped and the unflagged ones stayed put, you have done the job. If a sentence refuses to move, it is almost always still too general — go back and add the specific.

Keep the before and after. If you are ever asked about a flag, a version history showing your own edits is far more persuasive than a claim that you wrote it yourself. That record is what turns a detector disagreement into a documented editing process.

And keep the limits in view. A low score is not proof of authorship any more than a high one is proof of guilt. It is one signal, from one tool, on one day. The work of making the text sound like you wrote it is worth doing on its own merits — readers notice flat prose too.

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Measurement note: figures in this article come from our own detector run on 2026-09-28 — n = 709 hand-written guides versus n = 12 unedited model outputs, median AI score 25% and 34% respectively. Method: /methodology/.