--- title: "How AI detectors work, and why they are wrong so often" description: "Score text with the signals AI detectors use: predictability, burstiness, repetition, and style. Formulaic humans get flagged, edited model text slips by." published: 2026-07-18 updated: 2026-07-18 canonical: https://protrailblazer.com/posts/how-ai-detectors-work/ tags: [machine-learning, demo, ai-detectors, ai-detection, perplexity, burstiness, false-positives, tokens, llm] --- # How AI detectors work, and why they are wrong so often **Answer:** AI detectors never see who wrote a text. They measure statistical fingerprints, how predictable each token is, how uniform the sentence rhythm is, how much phrasing repeats, and how template-heavy the style is, then compare a fused score to a threshold. The demo below runs those same four signals over five specimens so you can watch a formulaic human essay get flagged while lightly edited model text slips under the line. The interactive below runs four heuristic signals over any text you paste or pick from five presets, fuses them into a model-like score, and lets you slide the decision threshold and run a labeled 24-sample dataset to watch the confusion matrix fill in. Try all five presets before reading on. *Interactive demo: [open the ai detectors demo](https://protrailblazer.com/demos/ai-detectors/)* ## What you're seeing The specimen panel at the top shows the text being analyzed, each word backed by a predictability strip: taller and redder means the word landed in a high-frequency, easy-to-predict position. Below that, a rhythm chart plots sentence lengths as bars, with a dashed mean line and a one-sigma band so you can see whether the rhythm is machine-flat or human-varied. The detector panel on the right fuses four toggleable signals into a MODEL-LIKE SCORE dial with a gauge needle and a shaded uncertainty band. The HUD tracks tokens analyzed, average predictability, sentence-length variation (as a coefficient of variation), n-gram repetition rate, and the final score out of 100. At the bottom, a threshold slider (default 65) draws a line across a scatter plot of the 24-sample dataset. Dots above the line are predicted AI; dots below are predicted human. Color tells you whether the prediction was right. The confusion matrix accumulates as you run the dataset, and the false-positive and false-negative chips update so the precision/recall tradeoff is visible, not asserted. The scoring uses transparent heuristic proxies: word-frequency tiers stand in for token probability, sentence-length coefficient of variation stands in for burstiness, repeated bigrams and trigrams stand in for repetition, and type-token ratio plus template connectives plus punctuation spread plus contractions stand in for style. The UI labels these HEURISTIC PROXIES and the uncertainty band ILLUSTRATIVE. No model runs and no text leaves the page. A few things to try: - **Load FORMULAIC HUMAN and run the scan.** The score lands at 69, above the default threshold, and a FALSE POSITIVE chip appears. Notice that the rhythm chart is as flat as a model's: five-sentence paragraphs, identical openers, a template structure a teacher drilled in. - **Switch to POLISHED MODEL.** It scores 68, just under the FORMULAIC HUMAN. The model output is actually less flaggable than the formulaic essay at default settings. - **Try NON-NATIVE ENGLISH.** It scores 70. The restricted vocabulary and simple sentence structures push predictability up; the detector has no way to distinguish a learner from a machine. - **With POLISHED MODEL loaded, run COPYEDIT then ADD VARIATION.** Watch the score fall from 68 to 58, below the threshold, triggering a FALSE NEGATIVE. Casual edits, not adversarial tricks, are enough to slip past. - **Slide the threshold from 30 to 90 while the dataset is running.** The false-positive and false-negative counts trade against each other continuously. No threshold zeroes out both on this 24-sample set. ## How AI detectors actually work A real AI detector is a classifier trained on a mix of human and model text. Its features are statistical properties of the text, and the most diagnostic ones fall into four buckets. Predictability (often called perplexity in the academic literature) measures how surprised a language model would be by each word or logit prediction in the sequence. A model generating text prefers high-probability continuations, so model output tends to have lower perplexity than human writing. The catch is that "high probability" and "formulaic" overlap heavily: the same words that make model output predictable also make student template essays and technical documentation predictable. Burstiness measures sentence-length variation. Human writers mix short punchy sentences with longer elaborated ones. Early language models produced very uniform rhythm. Most modern models no longer do, so burstiness is a weaker signal than it was in 2022, but it still appears in detector feature sets because the training data contains older model output. Repetition measures how often the same phrases recur. Model text at high temperature is less repetitive; model text at low temperature often repeats sentence openers, transition phrases, and structural clichés. The demo tracks repeated bigrams and trigrams plus duplicated paragraph-opener words. Style measures vocabulary richness (type-token ratio), the density of template connectives ("Furthermore", "In conclusion", "It is important to note"), the spread of punctuation marks, and the frequency of contractions. Formal academic register looks like model output on this axis; casual conversational writing looks human even when it is not. A real detector fuses these signals, usually via a fine-tuned classifier, and outputs a probability. The threshold that converts a probability into a binary HUMAN or AI verdict is a policy choice made by whoever deploys the tool. The same underlying score, say 0.62, reads as AI at a 0.60 cutoff and human at a 0.65 cutoff. No calibration is implied. To understand why token predictability matters, it helps to see how a model chooses its next word in the first place. The [autoregressive generation walkthrough](https://protrailblazer.com/posts/how-llms-generate-text) traces the full path from weights to next-token logits to a sampled output. And for how the temperature parameter shapes that probability distribution before sampling (directly affecting how predictable or surprising the output looks), see the [LLM temperature explainer](https://protrailblazer.com/posts/llm-temperature-explained). ## Why the false-positive problem is structural, not incidental Every signal the detector uses is a proxy for a property of model output, not a test of authorship. Predictability is really a fluency-and-formula proxy. Burstiness is a rhythm proxy. Repetition is a template-avoidance proxy. Style is a register proxy. None of them are causally linked to who pressed the keyboard. The writers who score highest on these proxies are people trained to write formulaically: students taught five-paragraph essay structures, non-native English speakers who learned formal written registers, technical documentation authors following a house style. In the demo, every human preset outscores the POLISHED MODEL at default settings. That is not a bug in the demo; it reflects what the signals actually measure. The false-negative side is equally structural. A single editing pass that varies sentence length, swaps template openers for concrete verbs, and introduces a contraction or two will drop most detector scores below any reasonable threshold. The demo's EDITED MIX preset scores 39 at default settings; it is the model-authored sample that was touched by a light copyedit. None of that constitutes adversarial attack; it is just editing. > **warn:** Detector output must never be the sole evidence in an academic, employment, or disciplinary decision. The false-positive rate for non-native English speakers is systematically higher than for native speakers on every major platform that has published error rates. A score above threshold is a reason to read more carefully, not a finding of wrongdoing. ## Frequently asked questions ### What does "perplexity" mean in AI detection? Perplexity is a measure of how surprised a language model would be by a sequence of tokens. Low perplexity means each word was highly predictable given the context; high perplexity means the choices were surprising. AI detectors use perplexity as a proxy because models generating text prefer high-probability (low-perplexity) continuations. The problem is that any fluent, formulaic writing, whether by a model or a human trained on templates, produces low perplexity. ### Can a detector tell the difference between a non-native English speaker and an AI? No. Non-native English writers often use restricted vocabulary, simple syntax, and formal register, exactly the features that detectors flag as machine-like. The demo's NON-NATIVE ENGLISH preset scores 70, higher than the POLISHED MODEL preset, because its signals overlap with model output on every axis. Several published audits have found false-positive rates for non-native writers significantly higher than for native writers. ### Why does editing the AI text so easily fool the detector? The detector measures statistics, not identity. Varying sentence lengths lowers the burstiness signal. Replacing template openers with concrete verbs lowers the repetition and style signals. Adding a contraction loosens the register signal. None of these changes require knowledge of how the detector works; they are just normal editing. The demo shows the score drop from 68 to 58 after COPYEDIT then ADD VARIATION, which is enough to fall below the default threshold. ### Is there a threshold that eliminates all false positives and false negatives? No, not on any dataset where the two populations overlap on the measured signals. The confusion matrix in the demo makes this visible: sliding the threshold from 30 to 90 continuously trades false positives for false negatives. The overlap is a property of the signals themselves, not of the threshold choice. A threshold is a policy decision about which error is more acceptable. ### Are the scores in the demo real detector scores? No. The demo uses transparent heuristic proxies labeled SIMULATED: word-frequency tiers stand in for token probabilities, sentence-length coefficient of variation stands in for burstiness, and so on. The purpose is to let you observe the structure of the problem, not to reproduce any specific commercial detector. Real detectors use trained classifiers and proprietary calibration. The directional behavior, such as formulaic humans scoring high and lightly edited model text scoring low, is based on what the published literature documents, not on this demo's specific numbers. ## Key takeaways - A detector never sees authorship, only statistics. Its score is a claim that the text looks like typical model output, and 'typical' is doing all the work: formulaic human writing and lightly edited model writing both land on the wrong side of it. - The decision threshold is a policy choice, not a property of the text. On the demo's 24-sample dataset no threshold zeroes out both false positives and false negatives; sliding it only trades one error for the other. - Predictability is largely a fluency-and-formula proxy, so the writers most at risk of false flags are non-native English speakers, students taught rigid templates, and documentation writers. In this demo every human preset outscores the machine-written POLISHED MODEL sample at default settings. - The signals are just as fragile in the other direction: one copyedit pass plus varied sentence openers drops the model-written sample from 68 to 58, under the default threshold. Casual edits, not adversarial tricks, are enough to slip past. - Score reliability collapses with length. Every signal is an average over sentences, so a verdict on a single short paragraph rests on almost no evidence, which the demo shows by widening its uncertainty band as the specimen shrinks.