How Do AI Detectors Work? Not Plagiarism but Patterns

An AI detector does not read for meaning the way people do.

Most AI content detectors use machine learning on large datasets of human-written and AI-generated text, analyzing features like sentence structure, word choice, and sentence length for patterns indicative of AI authorship.

Two common signals are perplexity and burstiness.

Perplexity is a measure of how predictable a piece of text is to a language model. AI-generated text typically has lower perplexity (more predictability) because AI models tend to choose the most statistically likely next word.

"Burstiness" refers to variation in sentence length and style. Human writing usually alternates short and long sentences (creating a varied rhythm), whereas AI-generated text tends to be more uniform and even in its sentence structure.

Modern AI detectors are machine-learning classifiers trained on very large datasets containing both human-written text and AI-generated text from various models, so the system can learn which linguistic features are correlated with AI-produced content.

As new, more advanced AI language models (e.g. GPT-4 and beyond) emerge and produce increasingly human-like text, AI detection tools must be frequently updated and retrained on the latest AI outputs to keep up.

AI detection only provides probabilities, not certainty. This means detectors sometimes give false positives (incorrectly flagging human-written text as AI) and false negatives (failing to catch AI-generated text).

Unusual human writing styles (for example, nonnative phrasing or an eccentric “voice”) can be misidentified as AI, while well-disguised or heavily rephrased AI-generated text may slip past undetected.

AI detectors and plagiarism checkers address distinct issues.

A plagiarism checker searches for text copied from existing sources by comparing the writing to a large database of published material, whereas an AI detector examines the text’s characteristics (style, sentence structure, predictability) to judge if it was AI-generated.

This means AI-generated text can be completely original (not found in any source) yet still get flagged by an AI detector, while a human-written passage copied from another source could evade AI detection because it wasn’t produced by an AI.

Detecting AI writing manually is still part of the process. Experienced editors and educators watch for telltale signs of AI-written text. For example, an overly generic, uniformly polite tone that feels “emotionally flat” and lacks a distinctive human voice.

They may also examine the document’s revision history or even keystroke logs to verify the writing process, since a transparent draft progression or typing record can confirm the work was genuinely written by a human.

Ultimately, human judgment is crucial: Even the companies behind detection tools stress that an AI score is just one signal and that knowing the writer’s usual style and using personal review is essential, especially if detector results are contested.

AI detectors for images and video (e.g. deepfake detectors) similarly work by analyzing telltale artifacts or patterns from generative models, using classifiers trained on known AI-generated examples versus authentic images.

However, just like text detectors, these visual detection systems only provide likelihood estimates and have significant accuracy limitations. They require extensive training data and can still produce false negatives or false positives, especially as new generative techniques emerge.

Major search engines (like Google) have made it clear that they care about content quality and usefulness, not whether a human or an AI wrote it.

In practice, online platforms aim to filter out low-quality, spammy, or unhelpful material regardless of how it was produced, rather than blanket-banning all AI-generated content.

Responsible use of AI-generated content typically involves transparency about AI involvement (e.g. disclosing or “citing” the use of AI where appropriate), rigorous human editing and fact-checking of the AI’s output, and infusion of real human expertise or insights before publication.

Notably, a high “AI-generated” score by a detector does not automatically imply the content is poor or unethical; AI-assisted content can be acceptable if it’s of high quality and vetted by humans.

Current AI detectors can provide a useful signal, but they are not foolproof verdict machines. Their output is just an indicator of writing origin, not definitive proof. In fact, as AI models become more sophisticated and human-like, detectors struggle more to distinguish AI text, and their reliability drops.

The responsible approach is to understand the limits of these detectors, avoid placing blind trust in a detection score, and always incorporate human judgment and review alongside any automated detection result

We created this article in conjunction with AI technology, then made sure it was fact-checked and edited by a HowStuffWorks editor.