How do AI detectors work? They detect AI-generated text using pattern recognition, linguistic analysis, machine & deep learning etc.
A number of tools, techniques, and algorithms are combined to detect and differentiate between AI-generated vs. human-written text.
We will discuss these AI-detection techniques in detail throughout this piece.
While the techniques remain largely similar, the accuracy of AI-detectors varies depending on how the detectors are trained and tested.
Let’s learn how these AI detectors work in detail.
Use of NLPs (Natural Language Processing)
Before we start to understand how do AI detectors work, let’s understand NLPs in brief. It’s important because a lot of AI–detection features, algorithms, and techniques used by tools rely on NLP.
NLP isn’t a feature by itself. It’s more like a field of AI that handles the communications between a computer, and humans. In other words, NLP is what makes a computer understand what a human is saying in any language. It can analyze grammar, and context, identify people, names, and more.
So, most of the ways that AI detectors use to detect AI-generated content are actually possible because of NLPs.
Let’s discuss how AI detectors work in detail then.
AI detection works by analyzing randomness/changes
Any good AI detector generally uses Perplexity & Burstiness in content to determine its origin. When I, you or any other human writes content, the text is unpredictable in terms of word choices. This is called Perplexity.
Similarly, Burstiness is this randomness/creativity/surprise but in terms of whole sentences. The human-written text doesn’t follow a very specific set of structure, grammar, sentence length, or anything else.
AI-written text on the other hand follows a coherent structure throughout. This coherence can be in terms of tone, grammar, or basically anything else.
In short, any text that has fewer variations, surprises, randomness, or changes throughout indicates its originality for AI detectors.
Of course, this is just one of half a dozen other AI-detection features.
Limitation:
The problem with perplexity is it’s very subjective. There’s no guarantee that a human being can not be structured. Also, there’s no definite amount of randomness a human shows. Hence, human text may sometimes be more or less random/surprising making this factor less accurate. AI models work by combining multiple factors together, let’s discuss the others.
Training Machine Learning Models using Classifiers
Machine Learning Models use pieces of code, called Classifiers (among other things), specializing in pattern recognition and predictions. They’re called Classifiers because they classify data into different groups, in this case, human-written or AI-generated.
AI detectors train their machine-learning models on massive amounts of data. This data is pre-classified into either human-written or AI-generated. The model then uses the patterns, structures and everything else to learn what separates these two.
Finally, with enough training, the model learns how to identify a text’s origin by looking for the patterns.
Limitations:
These models are heavily dependent on the datasets they’re trained on.
Linguistic Analysis
Linguistic analysis is simply an AI detection feature that analyses multiple linguistic factors in any given text.
- Understanding Semantics: Words do not always mean what they generally mean. The meaning is derived from the other words surrounding a word and the overall topic, tone, and other factors of the text. AI detectors analyze words to understand if the word is actually meaning what it should mean in that particular context.
- Understanding Syntaxes: Syntaxes in language are simply the rules that are applied to form a correct sentence. The grammar, word arrangement, and other technicalities are what this involves. AI detection tools analyze syntaxes to detect if the content is AI-generated or human-written.
- Word & vocab analysis: Human written content has a very different vocabulary as compared to AI-generated vocabulary. More importantly, any AI text generated using a prompt generally uses the same words and phrases for that generation as the prompt remains the same. Analyzing the phrases, their frequency, and the overall vocabulary tells AI detection tools the source of the content.
Limitations:
More advanced and new AI-generation tools can create more human-like text as they’re trained to avoid exactly these markers. Hence, linguistic analysis alone can’t paint an accurate picture of a text’s originality.
Deep Learning
Deep Learning is an advanced and much rarer AI-detection model that’s more thorough than the commonly available detection techniques.
It too is trained on a large set of data like machine learning. The difference is, that these models are trained to process data that’s closer to the way humans think and work. It uses a Recurrent Neural Network or Transformer to learn in more detail how human text differs from AI-generated text. Also, deep learning can extract features of the provided text on automation while machine learning requires some human intervention.
Hence, employing deep learning can help AI detectors detect texts that may be AI-generated but slightly altered or generated using advanced AI generators.
Limitation:
They’re not very common in the AI-detection industry for now.
Limitations with AI content detectors
You may have learnt how do AI content detectors work but we haven’t listed all the limitations these detectors face for now.
- Evolving technology: AI generators are constantly evolving. In fact, being AI-powered, they evolve almost by the second. Then there are entire teams and companies trying to improve their products. Hence, we do not have an AI detection tool that is 100% accurate for now.
- Results depend on the used generator: Each AI content generator has its own levels of output. Some are good at this job, others not so much. Hence, your AI detector tool may or may not be able to detect the originality of the source depending on which tool was used to create the content.
- Human edits: AI-generated content can be edited later to refine and iron the AI markers. This makes detecting them harder. Of course, they’re still as harmful as AI content as they can be detected at any time in the future when technology advances.
- False positives: Quite often, AI detectors may flag human-written content as AI-generated.
The crux of the discussion is that you must use AI detectors in combination with your own knowledge and skills for best results.
Frequently Asked Questions
Allow us to answer some of the most commonly asked questions about AI content detection:
Q. How do AI content detectors work?
AI content detectors primarily work by analyzing patterns, words, frequency or repetition of words to identify content that could be AI-generated, or human-written.
Q. Do AI content detectors work?
Yes. AI content detectors work and can help you spot AI-generated content. While there is not a 100% accuracy so far, they’re pretty accurate for the most part.
Q. Can I bypass AI content detection?
Yes. It’s very easy to bypass AI content detection as of now as the tools are new and there’s much to be learnt. However, remember that AI-generated content may harm you in the future even if it gets approved by your approval authority.
Conclusion
By now you probably have learned how AI detectors work. The basic concept is the same for most detectors, analyzing and recognizing patterns, structure, frequency, etc.
Of course, depending on the models used, training data and other factors the accuracy of these detectors varies.
For now, most detectors can detect purely AI-generated text easily, especially the ones generated with common, more popular tools. Probably with time, how do AI text work will evolve and get better at detecting.
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