How do AI detectors work? There are plenty of tools available today that promise to immediately identify whether content is generated by a human being, or a generative AI bot (like ChatGPT). But what actually allows these tools to differentiate between human and AI content?
On a broad scale, these tools essentially use AI to detect AI. They leverage various algorithms and language models to analyze content elements, and ask “Is this the kind of thing I would have created?” However, while various tools, like CopyLeaks and Originality AI, promise accuracy levels of up to 99%, the reality is that these systems aren’t as reliable as they seem.
Even the most advanced AI detectors can fail to detect AI – or worse, say that content is AI-generated when it was created by a human being.
Here’s everything you need to know about AI detectors, from how they work, to how accurate they really are.
What is an AI Detector?
An AI detector, or AI content detector, is a tool that uses artificial intelligence to identify whether text or content was created by a human or generated by AI.
They use machine learning models and large language models to analyze text and identify patterns that might indicate something was created by a bot. These tools have become pretty popular in the last couple of years, following the rise of generative AI.
In the business landscape, AI content detectors can help leaders who outsource content creation to other parties determine whether humans write the content they publish. They can also assist recruitment and HR teams in assessing the authenticity of resumes and cover letters.
In the educational world, AI detectors are a common tool for helping teachers and professors determine whether the content created by students is actually their own work. AI detectors can even help publishers and content creators ensure that they’re publishing content that “appears” human, reducing their risk of being penalized by search engine ranking algorithms.
Plus, some social media platforms and moderators are using AI detectors to flag and eliminate AI-generated spam and fake news.
How Do AI Detectors Work? The Methodologies
So, how do AI detectors work exactly? For the most part, AI detectors rely on the same principles, technologies, and models used in the AI writing tools they’re trying to detect. They use the same machine learning and conversational AI principles (like natural language processing), to process content, and identify “AI” red flags.
While there are various methods AI systems can use to detect AI-generated content, there are four particularly prevalent techniques used across the industry:
1. Leveraging Classifiers
Classifiers are machine learning models that sorts provided data into categories. They typically rely on pre-labeled training data. For instance, a developer will share text examples of “human-written” and “AI-written” content with a model that it can reference when analyzing new pieces of text.
Some more advanced classifiers, using deep learning solutions and neural networks, can also use unlabeled data for self-improvement. These models independently discover patterns and structures in examples, reducing the need to label data. However, they can also be less accurate than “supervised” models because they need to make “assumptions” themselves.
Whether they’re supervised or unsupervised, classifiers examine the main components of a provided piece of content, such as tone, style, and grammar, and then compare those to both AI and human written content. This allows them to determine which parts of a text may be AI-generated.
The analysis process usually involves a combination of strategies, including logistic regression, support vector machines, decision trees, and random forest methods. Once the analysis is complete, the classifier assigns a confidence score to the text, based on it’s potential “authenticity”.
These tools are limited in accuracy because they can only “compare” content based on the examples they’re given.
2. Assessing Embeddings
In large language models, embeddings represent phrases or words as “vectors” in a high-dimensional space. Each word is represented and mapped to a unique point based on its meaning and use in language. Then, the system creates a semantic “web” of meaning, placing words with similar meanings closer together in the space.
The vectorization process is essential because AI models can’t actually understand the meaning of words – they generally need them to be converted into numbers. Embeddings can be fed into a model designed for AI content detection and used to enable various types of analysis such as:
- Word frequency analysis: This identifies the most common words in a piece of content. A lack of variability and excessive repetition can be typical signs of AI-generated content.
- N-gram analysis: This analysis goes beyond examining individual worlds to capture language patterns and examine phrase structure. Human writing generally includes more varied N-grams and creative language choices.
- Syntactic analysis: This type of analysis looks at the grammatical structure of a sentence. Human writers generally produce more complex, varied sentences, while AI models usually stick to uniform patterns.
- Semantic analysis: Here, the AI model examines the meanings of phrases and words, looking for connotations, metaphors, similes, and cultural references. These nuances are usually more common in human-written text.
3. Measuring Perplexity
Most AI detectors work by looking for two specific things in content: perplexity and burstiness. Perplexity measures how “unpredictable” the text might be. Think of it this way, AI content generators, like OpenAI’s ChatGPT create text by examining existing content, then creating new content, choosing the most “likely” next words in a sequence.
Human beings don’t generally follow the same structured process. This means AI content is likely to be a lot more “predictable.” It will sound professional for the most part, but it’s relatively generic, with fewer creative language choices.
By assessing perplexity, AI models evaluate how likely they would be to be surprised by the words in a sentence. For instance, a sentence like “I couldn’t fall asleep last night” has low perplexity. It makes perfect sense.
A sentence like “I couldn’t fall asleep last time I went for a sleep study” is a little more surprising, so it has a higher level of perplexity.
On average, AI detectors assume high levels of perplexity mean content is more likely to be human-generated. However, that’s not always the case. Anything “out of place” in a text will trigger a high perplexity rating. For instance, if you fed an AI content detector a sentence like “I want orange juice for nice to meet you,” it would assume it was human-generated.
Similarly, if you fed the same tool a very predictable but correct sentence, like “I want orange juice for breakfast,” it might assume it’s AI–generated because it makes sense.
4. Identifying Burstiness
Burstiness is the other main thing that AI detectors look for when distinguishing between AI and human content. It’s similar to perplexity, but it focuses on entire sentences – like their structure and length. For instance, an entire article where all the sentences were about the same length, and followed the same structure would have a low level of burstiness.
An article with many different types of sentences, all featuring different lengths, levels of complexity, and structures, would indicate “high burstiness.”
Typically, AI text isn’t as “bursty” as human text. AI models produce sentences of almost always an “average” length. They follow the same standard structure when creating content, which means it often sounds monotonous.
Human beings, on the other hand, are a bit more creative. We use long and short sentences, different sentence structures, and complexity levels.
Again, while burstiness is good for identifying human and AI-generated content, it’s not ideal as a measurement of its own. If you know how to use AI for content generation, you can easily create prompts that affect the “burstiness” of the output.
For instance, you could tell ChatGPT to use a lot of short and long sentences in a random variation or to vary the complexity of sentences.
The Potential Alternative: Watermarking and Manual Tests
When you know how AI detectors work, it’s easy to see the problems they might have with accuracy. They’re just making educated guesses based on the data they have – for the most part. That’s why it’s a good idea not to rely on AI detectors completely.
You could look for “evidence” of AI content in text yourself. Checking for generic statements, overly polite or formal language, or inconsistency in a piece’s tone of voice can help you detect AI content. However, as AI content generators become more advanced and people learn more about prompting, it’s not easy to tell the difference between AI and human writing.
Some innovators are beginning to experiment with alternative “AI detection” options, though.
For instance, OpenAI has developed a system for watermarking ChatGPT-generated text. This would add an invisible watermark to anything created exclusively with ChatGPT. However, probably unsurprisingly, the company has chosen not to release this tool yet.
That’s likely because they don’t want to discourage people from using their application to generate text. If you knew you were likely to “get caught” using ChatGPT to write an essay, you’d probably switch to an alternative AI app.
Plus, there’s no guarantee that users wouldn’t find ways of “removing” these watermarks, which would make them ineffective.
How Do AI Detectors Work? The Technologies Behind Detectors
For now, regardless of the “method” AI detectors use to identify AI content, most tools are powered by two underlying technologies: machine learning, and natural language processing.
Machine learning allows AI detectors to identify patterns in large data sets related to contextual coherence and sentence structures. Depending on the specific training the model receives, the system can then detect AI content based on the absence or presence of specific patterns.
For instance, if a tool was trained on content generated by an AI model, any cross-over between patterns in the data set and the new content will signal AI content. Plus, machine learning allows models to perform “predictive analysis”. For instance, when searching for perplexity, models can “predict” how likely it is for a specific word to appear in a sentence.
On the other hand, natural language processing allows AI detectors to understand the structural and linguistic nuances of text and sentences. It ensures systems can understand that human-generated content is likely to be more creative and unpredictable than AI-generated content.
Natural language processing techniques also allow models to dive into the semantics of text, and assess the meaning behind words. Human writers generally understand more contextual “subtleties” when writing text than AI tools.
Do AI Detectors Work the Same as Plagiarism Checkers?
A lot of people (particularly educators and publishing companies), use AI detectors to check for signs of “plagiarism” in content. After all, models like GPT-4o in the latest version of ChatGPT still don’t allow AI systems to create totally novel content. Everything these tools create is based on existing content, making plagiarism a constant issue.
However, while AI detection tools and plagiarism checkers serve a similar purpose (detecting dishonesty in writing), they are slightly different.
AI detectors examine the full features within a piece of text to find patterns that match human-written or AI-generated text. The process involves many advanced technologies. Plagiarism checkers are simpler. They just “cross-reference” content with an existing database of resources, looking for direct hits or close similarities.
How Do AI Detectors Work? Example Tools
There are various different AI content detection tools available today, all with their own unique features. Here’s a quick run-down of some of the most popular options, and how they work.
TraceGPT
Otherwise known as AI Plagiarism Checker or the ChatGPT Content AI Detector, TraceGPT is a tool that analyzes characteristics in text that indicate the presence of AI. Like most content checkers, it uses methods to search for perplexity and burstiness and assigns a score to a piece of text based on how likely it is to be human-generated.
This tool is considered to be one of the most “accurate” on the web, but it still has limitations. On the plus side, it does come with an “authorship verification tool” built in.
Winston AI
Winston AI is a real-time AI content detection tool trained to recognize content created by tools like ChatGPT and Google Gemini. Like most AI content detection tools, it suffers from the occasional “false positive” or “false negative.” For instance, it can identify content created by bots like Claude as “human-generated.”
Like other AI detectors, this tool comes with some handy additional features for creators, too, such as a “readability scoring” system. Plus, you can use it to scan documents, pictures, and handwriting for insights into their authenticity.
Undectatable.AI
Widely considered one of the best AI detectors and a great tool for “humanizing” AI content, Undetectable.AI uses machine learning algorithms to analyze text. The tool’s creators are so confident in its abilities that they have a “money-back guarantee,” which promises to refund you if the tool gets the diagnosis wrong.
Still, it’s not perfect. It has an average “success rate” of around 95%, which is still good, but there’s still a decent chance this tool will make mistakes when analyzing your content. On the plus side, it is a handy tool for creating more “human-sounding” content.
GPTZero
GPTZero is a more specialized form of AI detector that focuses on finding content created by ChatGPT, Claude, Google Gemini, and Llama models. It uses a seven-layer system to identify patterns in content that indicate the use of specific AI tools.
Like most AI detectors, GPTZero measures perplexity and burstiness, which aren’t entirely reliable. So, it’s important to be mindful of the accuracy of its results. The good news for business leaders is that this tool integrates with many existing tools, like Google Docs and Microsoft Word, so it’s easy to use at scale.
Originality AI
If you’ve been searching for “How do AI detectors work,” you’ve probably encountered “Originality.AI” already. It’s one of the most popular AI detectors for publishers, writers, and agencies, trained to detect content from multiple LLMs.
Originality AI does have a bit of a reputation for regularly identifying “human” content as AI-written content. However, it’s pretty good at identifying content created by specific models, like ChatGPT. It also has a handy built-in fact checker, which can be useful for content creators who want to edit and upgrade their content.
Do AI Detectors Work Reliably? How Accurate Are They
Now you know how AI detectors work, you might be wondering how much you can rely on them. Ultimately, AI content detectors are limited in their accuracy. Although there are plenty of examples of models out there that promise high levels of reliability, many of these systems regularly generate false positives and negatives.
Some AI experts have even made statements saying that the current AI detectors we have today just aren’t reliable in practical scenarios. Many AI models are becoming more effective at “bypassing” the AI detection methods used by current platforms.
Advanced generative AI tools, combined with strong prompting strategies, can create content that “seems” human. At the same time, human writing can often be misinterpreted as AI-generated, particularly when it follows a predictable structure.
Identifying human-written content as AI-generated can be problematic for many reasons. If Google’s algorithms can’t determine whether a piece is authentically human-written, companies could lose their ranking in the search engine result pages.
It can also damage the reputations of writers and publishers and create mistrust among audiences who want to know for certain they’re reading human-created content.
Furthermore, false “positives” can lead to serious issues for students. They can be accused of cheating or plagiarizing text, which can cause them to fail tests and examinations.
The Future of AI Content Detection
AI detection tools will become increasingly popular as people worldwide continue to generate more content with artificial intelligence. While these tools claim to offer accurate insight into whether humans or bots create content – we can’t trust them completely.
Learning how AI detectors work offers an insight into the various mistakes these systems can make and shows us that we must take their results with a pinch of salt.
Still, AI detectors have value. They can help with the editing stage of content production. They can also assist writers in ensuring their content sounds as “human” as possible. Some tools even come with extra features that help with assessing readability or checking facts to help writers improve the quality of their content.
The important thing to remember is that AI content detectors are still in their infancy. Like any AI tool, they’re not perfect, and they do make mistakes.
