Have you ever wondered if AI content detectors, sometimes known as ChatGPT detectors, actually work? The short answer is yes, but not always, and it really depends on your use case and the tool you are using.
If you’re genuinely interested in leveraging these tools effectively, this post is perfect for you. We will explain the basics behind these nifty tools, helping you understand how they function.
We will also discuss what the future holds for detecting AI-generated text and how these methods will enhance the current capabilities of these tools, making it more difficult to pass AI-generated content as human-generated.
Lastly, we will touch on the topic of reliability—how reliable are the current AI content detectors, and how can you ensure that you choose the right one for your needs?
At a basic level, content detectors operate similarly to the tools used to generate text in the first place.
ChatGPT-like tools work by trying to understand your prompt and then generating a string of words that they predict will best answer your question, based on the data they were trained on.
AI content detectors operate on the same principle but analyze the input text and essentially ask, “Is this something that I would have written?” If the answer is “yes,” the detector concludes that the text is AI-generated.
Currently, there are two key factors that AI content detectors look for in a text: perplexity and burstiness.
Thus, the main differences among AI content detectors themselves, aside from their training data, lie in what they look for when analyzing and comparing text patterns.
Generative AI is still in its early phases, as are AI content detectors. However, as one might have noticed, generative AI isn’t warmly welcomed everywhere.
For instance, in academia, professors struggle with students who rely solely on AI to generate their papers, and the web is filled with thin content just to score extra SEO points.
There is some public pressure that will likely prompt regulation of AI-generated content in the future. One method of enforcement could be nudging the companies that develop ChatGPT-like tools to incorporate changes.
Introducing AI Watermarks: AI watermarking is a method of adding a special, unique mark to the output of an artificial intelligence tool, such as rare words or phrases in a text.
AI watermarking is a relatively new technique, but it is already growing in popularity. It is expected to be implemented in areas where human integrity is essential.
However, as with every new development, there are hurdles that are being worked on. Perhaps there will be a breakthrough in the coming months. Who knows? The world is changing fast.
But if you are interested in AI watermarking, you can check out this insightful article by TechTarget.
From our experience, the reliability depends on the text you are working with. For almost all cases, the shorter the text, the harder it is to analyze and thus to detect if it was AI-generated or not.
Also, there’s no one tool that’s best for everyone:
Remember that the most reliable and trustworthy AI content detectors are designed to work for specific customers, so choose yours wisely—whether you are a marketing specialist or someone working in academia, there is an option for everyone.
To give a figure, the better-paid options can achieve around 85% accuracy, while some free “do-it-all” tools might only reach just under 70%.
Now you know how AI content detectors work. Kudos to you!
If you are someone trying to avoid detection, at least you are making an effort by reading this post. However, keep in mind that AI detectors are evolving, and someday your paper could be flagged as AI-generated, even if it passed years ago.
As with everything, when choosing, it’s important to do your homework. Keep in mind your specific use case and choose accordingly—you don’t need an academic tool if you are a marketer. And don’t fall for tools that promise to do everything; try to see through the marketing jargon.
If you don’t know where to start, check out our directory of AI content detectors here.