How AI Detection Actually Works Under the Hood

AI content detection has become a billion-dollar industry, but few people understand how these tools actually work. This deep dive explores the technology, mathematics, and limitations behind the most popular AI detectors.

At their core, AI detectors are classifiers trained to distinguish between human and AI-generated text. They analyze statistical properties of text that differ systematically between the two sources. The two most important metrics are perplexity and burstiness.

Perplexity measures how "surprised" a language model is by a piece of text. Human writing tends to have higher perplexity because we make unexpected word choices, use idioms, and vary our style. AI text has lower perplexity because it consistently chooses the most probable next word.

Burstiness measures the variation in sentence complexity throughout a text. Humans naturally write with high burstiness, mixing simple and complex sentences. AI produces more uniform sentence structures, resulting in lower burstiness scores.

GPTZero, one of the first AI detectors, primarily relies on these two metrics. Originality.ai uses a more sophisticated approach, training a classifier on millions of examples of both human and AI text. Turnitin combines language model analysis with their existing plagiarism detection infrastructure.

The limitations are real and important. All detectors have false positive rates, meaning they sometimes flag human-written text as AI-generated. Non-native English speakers are disproportionately affected because their writing patterns can resemble AI output. This has led to serious ethical concerns in academic settings.

Understanding these mechanics is key to effective humanization. By increasing perplexity and burstiness, tools like WriteHumane shift the statistical properties of AI text into the range that detectors classify as human.