How AI Detectors Separate Human and Machine-Generated Content

How AI Detection Works

More and more of the text and video content online is generated by AI tools that create realistic-sounding text and natural-looking videos. So how can writers tell if what they're reading was made by a human or a machine? The answer isn't as simple as spotting overused em-dashes, despite popular belief. Many human writers overuse certain punctuation, making it an unreliable clue.

Instead, AI detection focuses on phrasing patterns and repetition. Large language models often repeat themselves or use certain phrases more frequently than humans do. This is the principle behind AI-detection programs. However, these systems are often powered by AI themselves and rarely reveal how they reach their conclusions, which raises trust issues.

A company called Copyleaks offers a feature named AI Logic that aims to provide clearer insights. It highlights specific passages and explains whether they match known AI-generated text or contain phrases typically used by AI. This approach resembles plagiarism detection but targets AI writing instead.

AI writing is everywhere now. Tech giants like Microsoft and Google integrate AI helpers into workplace apps, and even dating apps use AI to improve profiles or messages. A survey from the Kinsey Institute and Match found that 26% of singles use AI in dating. Given this, writers may want tools to verify if content is genuinely human-made.

Copyleaks' approach moves detection forward by offering transparency. But the key still lies with the human reviewing the data to decide what’s a coincidence and what’s a real sign of AI involvement. As Copyleaks CEO Alon Yamin puts it, the goal is to provide as much evidence as possible to remove doubt.

How AI Detection Works Under the Hood

Copyleaks began by using AI to detect copyright infringement by analyzing writing styles. When ChatGPT appeared in 2022, the company adapted these methods to detect AI-generated writing styles. They trained models to observe sentence length, punctuation patterns, and specific phrase usage. This can be described as "AI versus AI."

The challenge is that large language models act like a black box: they produce coherent outputs without revealing the internal process. Copyleaks’ AI Logic tries to increase transparency by showing which parts of the text might be AI-generated.

AI Logic uses two main strategies:

• AI Source Match: This compares the text to a database of AI-generated content collected from Copyleaks and other AI-produced sites, working like a plagiarism checker.
• AI Phrases: This identifies phrases that research shows are far more common in AI-generated text than in human writing. For example, the phrase "with advancements in technology" appears 125 times per million AI documents but only 6 times per million human-written documents.

Testing AI Detection in Practice

Human-Written Classic

To test the system, a section from Isaac Asimov’s 1956 short story The Last Question—about a fictional AI solving a complex problem—was analyzed. Copyleaks correctly identified it as 100% matched to existing online text and 0% AI-written.

Partially AI-Written Text

Adding AI-generated paragraphs to an original article and running it through Copyleaks showed mixed results. When ChatGPT was used for additions, Copyleaks detected 65.8% of the text matched existing online content but failed to flag the AI-written paragraphs. However, using Google’s Gemini to add copy resulted in Copyleaks flagging 100% of the text as potentially AI-written, including parts originally written by a human.

Fully AI-Written Text

In a fictional news story where the Cincinnati Bengals won the Super Bowl (which hasn’t happened in reality), Copyleaks identified the text as entirely AI-written. However, it didn’t highlight specific phrases responsible for the detection, only noting "other criteria" suggested AI generation.

A second AI-generated story about the Bengals provided a more detailed report, flagging phrases like "made several critical" and "testament to years of" as more common in AI text. Similarly, a story about the Los Angeles Dodgers winning the World Series was marked 100% AI-generated despite its factual possibility.

High-Profile Example: Questionable Report

Copyleaks also analyzed a controversial report from the Trump administration's Make America Healthy Again Commission. The report cited academic studies that researchers said didn’t exist. Copyleaks found 20.8% of the report potentially AI-written and flagged phrases commonly used in AI text, such as "impacts of social media on their" and "The Negative Impact of Social Media on Their Mental Health."

Can AI Reliably Detect AI-Written Content?

Transparency in AI detection tools like Copyleaks is a step forward, but the technology is not foolproof. False positives remain a concern, especially when human-written text gets flagged due to certain phrases. Yet, these tools can catch entirely fabricated content.

Copyleaks CEO Alon Yamin emphasizes that the goal is not to deliver absolute truth but to assist humans in making informed decisions. AI detection tools provide data, but human judgment is crucial to interpret the results accurately.

For writers, the best advice is to maintain their unique voice and style. Occasional flagged phrases may be harmless, as some expressions are naturally common across both AI and human writing. But if several paragraphs are flagged, that’s a signal worth investigating.

Writers interested in improving their understanding of AI and its implications on content creation may find value in specialized courses. For practical AI training and resources tailored to creative professionals, consider exploring Complete AI Training's courses for writers.