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4 Ways AI Content Detectors Work To Spot AI

AI content detectors use machine learning and natural language processing to inspect linguistic patterns and sentence structures to find out if it's AI-generated or human-written.

Classifiers put text into groups based on patterns they have learned, and embeddings represent words as vectors to show how they relate to each other semantically.

Perplexity measures how predictable the content is, with higher levels indicating that it was written by a human author. Burstiness checks how different sentences are, with human writing showing greater diversity.

AI detectors are useful, but they are not perfect.

They can often lead to false positives or rejections, so it is best to review their reports manually.

What you will learn

  • What AI content detectors are, and how AI detection works
  • Which methods and technologies are used to detect AI-generated text
  • How reliable AI detection tools are
  • How AI content detection tools differ from plagiarism checkers

What is an AI content detector?

AI content detectors are tools that process and examine text in real-time to determine whether AI was involved in some way or all of it.

They do this by analyzing the content's linguistic and structural features (semantic meaning, sentence structure, language choices, etc.) or comparing the text to existing datasets of content created by artificial intelligence or humans to differentiate between the two.

There are several reasons AI content detectors have become so popular recently.

If you're a business owner who decided to outsource content writing, AI content detection can be immensely helpful in ensuring the content you receive isn't created mindlessly using an AI tool.

AI content detectors can also help uncover academic dishonesty.

Since the rise of AI-generated content, numerous schools and universities have implemented them to combat different forms of cheating, most notably essays created by AI without proper research.

Such tools can also improve the peer-reviewing process to rid academic publications of low-quality or inaccurate pieces.

These are only some of the many cases in which an anti-AI detector can be used used.

Let's see how they provide the aforementioned benefits.

How accurate are AI content detectors?

AI content detectors are reliable 7 out of 10 times on a sample size of 100 articles. While they're undoubtedly helpful in detecting AI-generated content, you should manually review their results for greater accuracy.

With all the hype surrounding various AI solutions, it's easy to forget they've only been around for a short while.

But this hasn't stopped the debate around AI content detection with some swearing by it, while others dismiss it as fantasy.

Like generative AI tools, AI detectors are still in their infancy and are constantly evolving.

There are several reasons for this, the most notable one being language nuances and creativity.

AI detectors don't understand language as well as humans do—they only rely on historical data from their training sets to make predictions as confidently as possible.

The result isn't always accurate, so you might encounter false positives and negatives.

While AI writing tools are undoubtedly useful, they shouldn't be used without human supervision due to inaccuracies, also known as AI hallucinations.

For example, when we tested Originality.ai using 100 human and AI articles, between 10% and 28% of human-written pieces were categorized as AI-generated.

Another significant challenge is the rapid evolution of AI text generators, which AI detectors might struggle to catch up with.

Some advanced AI content generators like Surfer AI can bypass AI detection to a significant extent, further blurring the line between human and AI content.

We'll likely continue to see this constant race between AI generators and detectors, so you shouldn't rely 100% on content detection tools.

Still, many people rely too much on AI to create content, which can cause various issues.

Publishing unverified AI-generated content online can lead to misinformation.

While Google has nothing against AI content on its own, pieces that contain factual errors or otherwise aren't valuable almost certainly won't rank highly in search engines (as they shouldn't).

This is particularly true for YMYL (Your Money, Your Life) topics, where accuracy is paramount.

Still, using an AI detector is far superior to detecting AI writing manually, as doing so is way too time-consuming and can be challenging even for highly skilled content managers.

But remember to take their results with a grain of salt.

4 ways AI content detection works

AI detectors rely on many of the same principles and technologies as AI text generators. Machine learning (ML) and natural language processing (NLP) are among the most important ones, as they allow a detection tool to process input and differentiate between AI-generated and human-written content.

While this can be done in various ways, these four techniques are particularly prevalent for AI content detecting tools.

1. Classifiers

As the name implies, a classifier is an ML model that sorts the provided data into predetermined categories. It often relies on labeled training data, which means it learns from text examples that have already been classified as human or AI-written.

The classifier then uses the patterns from the training data to sort new text accordingly.

A classifier can also use unlabeled data, in which case it's referred to as unsupervised.

Such models discover patterns and structures independently, which means they're less resource-intensive because there's no need for lots of labeled data.

On the flip side, unsupervised classifiers might not be as accurate as their supervised counterparts.

Regardless of the type, a classifier examines the main features of the provided content (tone and style, grammar, etc.).

It then identifies patterns commonly present in AI content and human-written pieces to draw a boundary between the two.

Depending on the model used, the boundary can be a line, curve, or another shape.

Some of the most common machine learning algorithms used by classifiers include the following:

  • Decision Trees
  • Logistic Regression
  • Random Forest
  • Support Vector Machines

When the analysis is complete, a classifier assigns a confidence score that indicates the likelihood of the provided text being generated by an AI writing tool.

Note that the results might not always be perfectly accurate, as classifiers can show false positives.

For example, if a model is trained on a specific type of human writing and overfitted, it might stick too closely to the training datasets and categorize anything that deviates from them as AI-generated.

To avoid such issues, classifiers should be updated regularly and follow the evolution of AI-generated content.

2. Embeddings

Embeddings are used to represent words or phrases as vectors in a high-dimensional space. This might sound pretty esoteric at first glance, though it's easy to grasp if you understand two concepts:

  1. Vector representation—Each word is represented and mapped to a unique point based on its meaning and usage in language.
  2. Semantic web of meaning—Words with similar meanings are placed closer together, forming a semantic web.

Vectorization is so important because AI models don't understand the meaning of words, so they must be converted into numbers and represented as explained above.

Embeddings can then be fed to a model designed to differentiate between AI and human-written text. This is done through several types of analysis, most notably:

  • Word frequency analysis—Identifies the most common or frequently occurring words in a piece of content.
    Excessive repetition and lack of variability are common signs of AI-generated content, as AI writing tools tend to rely on the most statistically common words or phrases.
  • N-gram analysis—Goes beyond individual words to capture common language patterns and analyze phrase structure in a given context.
    Human writing typically involves more varied N-grams and creative language choices, while an AI model might fill the text with too many clichéd phrases.
  • Syntactic analysis—Examines the grammatical structure of a sentence.
    AI tools often use uniform syntactic patterns, while human-written text shows greater syntactic complexity and varied sentence constructions.
  • Semantic analysis—Analyzes the meaning of words and phrases, taking into account metaphors, connotations, cultural references, and other nuances.
    AI content often misinterprets such nuances or omits them from the text altogether, whereas a human-written piece shows greater depth of context-specific meaning.

Effective AI-generated content detection involves a combination of these analyses, which can be quite resource-intensive.

High-dimensional data is also quite complex—visualizing and interpreting embeddings can be difficult with hundreds or thousands of dimensions.

This calls for simplifying and reducing dimensionality, which is no easy feat.

3. Perplexity

Perplexity is a measure of how surprised (perplexed) an AI model is when encountering new text.

Think of it as a litmus test of the provided content's "humanity."

If an AI model is surprised by the language choices, it means the text deviates from something it could've created.

With this in mind, an AI detector relying on perplexity is likely to classify predictable content as AI-generated.

If the provided text has higher perplexity, it's more likely to be written by a human.

Now, high perplexity isn't always a result of more creative language choices.

Anything that seems out of place will trigger it, so perplexity may not be the most precise AI detection method due to false positives.

If you feed an AI detector a bunch of nonsensical sentences and jibberish, it will be perplexed regardless of whether the text was written by a human or a machine.

Similarly, a newbie writer might use predictable sentence structures and chlicéd phrases due to a lack of experience or well-developed vocabulary.

An AI detector might classify their content as AI-generated because it didn't have trouble predicting what comes next while analyzing it.

This is why it may not be the best idea to rely on perplexity as a standalone detection method.

It's more accurate when paired with contextual analysis, as the model will have a better understanding of the meaning behind the text instead of only focusing on the ease of prediction.

4. Burstiness

Burstiness is similar to perplexity, though it focuses on entire sentences rather than specific words.

It measures the overall variation in sentence structure, length, and complexity, as these features can vary greatly between AI-generated and human-written text.

AI generators tend to produce more monotonous text with lower burstiness.

They might also repeat certain words or phrases too frequently because they've seen them appear often in their training data.

All of this results in uniform sentences without much creativity or complexity, so an AI tool's content might appear pretty dry.

Humans, on the other hand, typically create far more dynamic content.

You'll see a balance of short and long sentences with varying structures and complexity levels, which contributes to high burstiness.

While burstiness is a crucial separator between human and AI content, it's best not to focus solely on this factor.

With a good prompt, you can instruct an AI text generator to create more complex text with varied sentence structures, which might trick a detection tool that relies too heavily on burstiness.

A capable AI detector should use burstiness as one of the several criteria for recognising AI-generated text as such a comprehensive approach is more likely to provide accurate results.

Key technologies behind AI content detection

Regardless of the method used to detect AI content, two technologies play a central role in the process:

  1. Machine learning
  2. Natural language processing

Let's briefly touch on how these technologies support AI detection tools.

Machine learning

Machine learning lets AI detectors identify patterns in large datasets.

Such patterns can relate to the content's sentence structures, contextual coherence, and many other features that separate human-written content from pieces generated by an AI tool.

Depending on the model's specifics and the datasets it was trained on, AI content can be detected by either the presence or absence of familiar patterns.

For example, if an AI detection tool was trained on content generated by an AI language model, any crossover between the patterns identified in the training dataset and new content will be a signal of AI-generated content.

Besides identifying patterns, machine learning enables predictive analysis—the ability to correctly assume which word should appear next in a sentence.

Perplexity relies heavily on predictive analysis, as a lack of "surprises" during prediction indicates the use of AI.

Natural language processing

Much like NLP is crucial for AI text generation, it can be used to detect AI-generated content.

It lets AI detectors understand the many linguistic and structural nuances of the provided text and the context and syntax of sentences.

These are some of the main features that separate human and AI content.

AI content might not be as stylistically rich as a human-written piece that will contain creative linguistic choices and contextual cues that most AI writing tools miss.

Natural language processing techniques are also used to dive into the semantics of the provided text and assess the depth of meaning.

This is another aspect of content creation where human writers have a significant advantage, as AI models might fail to understand the contextual subtleties that make a world of difference in a text.

Besides ML and NLP, several supporting technologies enable AI detection, most notably:

  • Data mining—Helps AI tools detect patterns by extracting them from large datasets
  • Text analysis algorithms—Scrutinize the structure and stylistic elements of the given text to help an AI detector assess the content's main elements (length, complexity, vocabulary usage, etc.)

AI detectors vs. plagiarism checkers

AI detection tools serve the same general purpose as plagiarism checkers—uncovering dishonesty in writing. Still, these tools are vastly different in their underlying mechanisms.

As explained, AI detectors work by examining the provided text's features to find patterns consistent with AI or human-written text. This process is quite complex and involves various advanced technologies and processes.

A plagiarism checker is much simpler.

It cross-references content with an existing database of resources, trying to find direct hits or close similarities. Depending on the technique used, plagiarism checkers can look for keywords, phrases, or specific content fragments that appear in the database.

Note that most AI writing tools—capable ones, at least—are trained to avoid plagiarism.

They might still create derivative content, especially without sufficient input and elaborate prompts.

How to pass AI content detection

Surfer AI allows you to create search engine-optimized articles that are written in a human-like manner and can pass AI content detectors.

Of course, we know that Google and other search engines don't punish AI content unless it has been created solely to capture rankings and traffic.

From Google's Search Central blog post on AI content guidelines,

But it may help you to use AI content to speed up your content creation process while keeping costs and effort manageable.

Using Surfer's anti-AI feature can help you write content that passes off as human-written.

Surfer will then generate an outline that you can edit and instruct the writer to touch upon specific information.

In about 20 minutes, you'll have an article that passes AI content detection and is ready to publish.

Let's paste the text into Originality's AI detection tool to see if it passes AI content detection.

And here is Originality's AI detector score after a scan.

98% human written and 2% AI.

Key Takeaways

  • AI content detectors scrutinize the content's linguistic and structural features to determine whether it was written by a human or an AI text generator.
  • AI text detection is crucial for uncovering low-quality content that shouldn't be published without editing and fact-checking. It's also useful for detecting deceptive academic practices and the overuse of AI in research papers and similar publications.
  • Classifiers are among the most commonly used AI detection tools. They identify patterns commonly found in AI-generated or human-written text to draw a clear boundary between the two.
  • Embeddings play a crucial role in AI content detection. Using a combination of different types of analyses, tools that rely on them can analyze several key features of the provided text to determine if it was written by AI.
  • Perplexity and burstiness are some of the most important differentiators between human and AI-generated text. More predictable and monotonous sentences are often a tell-tale sign that AI was involved in the content creation process.
  • Much like AI text generators, AI detection tools revolve around NLP and ML. The former enables a thorough analysis of the content's linguistic and structural features, while the latter allows for pattern detection and recognition.
  • As useful as AI content detectors are, they shouldn't be trusted blindly. False positives and negatives are still a significant issue, so don't take the results for granted.
  • AI detectors are often used in conjunction with plagiarism checkers to identify different types of writing dishonesty. While the two technologies share the same purpose, they have completely different underlying mechanisms, with AI detectors being far more advanced in terms of the technologies used.

Conclusion

AI detection is among the main challenges in today's content landscape. It is becoming more challenging to distinguish between human and machine writers as AI writing tools advance.

Still, AI text generators can't understand the many intricacies of a language as well as humans, which a capable AI detection tool can pick up on.

If you plan on using one, remember not to trust its judgement 100%. No technology is infallible, so spend some time assessing the content yourself instead of just looking at the score that pops up.

After all, the quality of the piece and the value you get from it matter the most.

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