The use of AI has expanded significantly. Today, this technology improves user experience by providing them with various AI tools to achieve their goals, ranging from entertainment to work processes. However, AI, while offering these opportunities, also brings challenges when it is used instead of authentic human creativity in the content creation process. In this article, we will analyse the operating principles of AI detectors and how they can distinguish human creativity from machine-generated content.
The Need for AI Detectors Tools
As AI continues to dominate the market, different user groups are discovering its benefits. One of these groups is those using text generation tools, which can instantly produce any type of content from a text prompt. When we talk about business communication, marketing, and similar fields, this AI use case seems quite convenient and does not spark much debate, although it can still be questioned. However, things change when we start discussing original works: written books, student papers, or scientific research, where authentic ideas and unique work should be present.
According to Statista, in July 2024, the share of students using AI for school assignments worldwide reached 86%, with a quarter of them using AI daily. This raises the biggest discussion about the hidden use of AI in academic papers, where the student’s knowledge is being evaluated. This leads to a need for quality tools that can detect AI-generated content and alert users to the likelihood of its use. In this situation, AI itself is used, with its algorithms helping to address this challenge.
Operating Principles of AI Detectors Measures
AI detectors are tools that allow users to check for possible AI involvement in texts by examining their linguistic and structural features. The working principle of AI detection tools is based on machine learning and natural language processing (NLP). In this context, it is important to discuss the four main principles that dominate such tools: classifiers, embeddings, perplexity, and burstiness. Below, we will analyse them.
Classifiers
These are machine learning models that structure data into specific categories — this analysis examines key content elements such as tone, style, grammar, etc. In this way, AI algorithms identify patterns typical for AI-generated content and those found in human-created content, and based on that, they can determine the threshold where AI might have been used by assigning a score.
Embeddings
In this process, AI detectors use numerical values and vectorization to enable the system to understand the provided text. The principle of this analysis examines relationships between words, context, and so on. It looks at whether the same words repeat too often, the structure of phrases, grammatical forms, inclusion of cultural nuances in the text, etc.
Perplexity
This process is based on how predictable the text is. At this level, the sentence structure is considered — AI-generated sentences are often very simple, short, and clear. On the other hand, human-written content can have more complexity, potentially more errors, but the sentences should still be logical.
This dimension can be controversial, as it is important to critically assess that, depending on the context, the text creator has varying levels of knowledge, vocabulary, language nuances, etc., while AI can also generate complex texts that may not be very logical.
Burstiness
Focus is placed on sentences, analysing their coherence, length, sustained complexity, or whether they are simple. AI more frequently creates monotonous text, which can be identified as an intelligently generated result.
Are AI Content Detectors Reliable?
The effectiveness and reliability of these tools can be debated. Not all AI detectors are equally accurate or show the real likelihood of AI use. The operation of these tools is based on data that needs to be constantly updated to achieve the best results, as well as the AI-induced hallucinations that also pose certain challenges.
In the study by Elkhatat et al., the performance of some AI detection tools proved that AI systems are not always 100% accurate and are more likely to make mistakes with texts generated by the latest AI programs (Elkhatat, Elsaid, & Almeer, 2023). This is confirmed by another study by Odri & Yoon, which highlighted that certain modifications in the text, such as paraphrasing, removing commas, etc., could help hide the use of AI in the text (Odri & Yoon, 2023).
Final Word
AI detectors can be a useful means of identifying AI-generated content. However, it remains important to critically assess the results provided by these tools each time, with human intervention being necessary to evaluate them further, as different tools can vary in reliability.
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Sources: BMC, Science Direct, Statista, SURFER, QuillBot
