AI detectors, also known as AI writing detectors or AI content detectors, are specialized tools designed to identify text that has been partially or entirely generated by artificial intelligence (AI) models, such as ChatGPT. These detectors serve multiple purposes, from verifying the authenticity of written content to filtering out fake product reviews and spam. In this blog post, we’ll explore the principles behind AI detectors, their current reliability, and the scenarios in which they can be applied.

How Do AI Detectors Work?


AI detectors typically rely on language models that resemble the ones used by the AI writing tools they aim to detect. The core principle involves the model assessing a piece of text to determine if it resembles something it would generate itself. If the answer is affirmative, it suggests that the text may be AI-generated.

AI detectors focus on two key variables within a text: perplexity and burstiness. Lower values of these variables indicate a higher likelihood that the text is AI-generated. Let’s clarify what these terms mean:


Perplexity measures how unpredictable a text is, gauging its potential to confuse or perplex an average reader. In other words, it quantifies how sensical and natural the text reads.

  • AI language models aim to produce texts with low perplexity, as they are more likely to make sense and read smoothly, but they are also more predictable.
  • Human writing tends to exhibit higher perplexity due to more creative language choices, albeit with occasional typos.

Language models operate by predicting the next word in a sentence, selecting the most fitting option. For example, in the sentence “I couldn’t get to sleep last…,” different continuations have varying degrees of plausibility.

Low perplexity is indicative of AI-generated text.


Burstiness measures the variation in sentence structure and length, akin to perplexity but focused on sentences rather than individual words.

  • Texts with minimal variation in sentence structure and length have low burstiness..
  • Texts with diverse structures and lengths exhibit high burstiness.

AI-generated text typically displays less “burstiness” compared to human text, resulting in sentences of average length with conventional structures. This tendency sometimes makes AI-generated writing appear monotonous.

Low burstiness suggests that a text is likely AI-generated.

A Potential Alternative: Watermarks

OpenAI, the organization behind ChatGPT, is actively exploring a “watermarking” system for AI-generated text. This system would involve embedding an invisible watermark into AI-generated content, allowing for its detection by another system to confirm its AI origin.

However, this watermarking system remains in development, with details on its functionality and effectiveness yet to be fully disclosed. It’s also unclear whether these proposed watermarks will persist if the generated text undergoes editing. While this method shows promise for future AI detection, many uncertainties still surround its implementation.


How Reliable Are AI Detectors?

In practice, AI detectors often perform well, particularly with longer texts. However, they can falter when faced with AI output that has been deliberately made less predictable or when text has been edited or paraphrased after generation. Additionally, detectors can occasionally misidentify human-written text as AI-generated if it aligns with the criteria of low perplexity and burstiness.

Our research into AI detectors indicates that no tool can guarantee complete accuracy. The highest accuracy we found was 84% in a premium tool or 68% in the best free tool. While these tools provide valuable insights into the likelihood of AI generation, it’s crucial not to rely on them as sole evidence.

As language models continue to evolve, detection tools will continually need to adapt to keep pace. Even the most confident providers acknowledge that their tools cannot serve as definitive evidence of AI generation. Universities and academic institutions, for the time being, maintain a cautious stance towards relying on these tools exclusively.


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