There are different approaches to detecting AI-written text—from supervised classifiers and perturbation-based probability tests to perplexity measures and watermarking—and this tutorial builds a model returning a 0-100 score.

The author outlines the detection approaches and introduces the specific method used: a classifier that returns a 0-100 score indicating how likely text is AI-generated. ✦ AI generated

the author · Ahead of AI · 2026-08-15 · original ↗

In essence, there are different ways to detect AI-written text, from supervised classifiers and perturbation-based probability tests to perplexity measures and watermarking. In this tutorial, we will build a model that returns a 0-100 score. It's essentially a classifier with an estimated probability score. The probability score will denote how likely a text is AI-generated according to the classifier. (Or, to be precise the score is the classifier's estimated probability for the AI-generated class based on its training distribution. However, we shouldn't interpreted it as a general probability that the text was written by AI.)

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