# Towards AI-Generated Essay Classification Using Numerical Text Representation
## 2 Related Works  
Detection tools for AI-generated texts can be classified into black-box detection and white-box detection [1]. Black-box detection methods access large language models at the API level, collecting samples of human- and machine-written texts to train a classification model. Initially, these detectors were highly effective, as AI-generated texts often exhibited linguistic or statistical patterns. However, as the models evolved, their precision decreased [1]. White-box detection, on the other hand, provides full access to the target language model, allowing for the monitoring and control of its behavior. Black-box detectors are typically external solutions, while white-box detectors are often tools developed by the language model's creators [1]. The three main stages in black-box detection are data acquisition, feature selection, and the construction of classification models. The effectiveness depends largely on the quality of the acquired data. Feature selection techniques detect variables considering statistical differences, linguistic patterns, or fact verification. Commonly used classifiers include logistic regression, support vector machines, naive Bayes classifiers, decision trees, and random forests [4]. Basic methods offer interpretability, allowing researchers to analyze the significance of the input variable and understand the classification decisions of the model [1]. Recent studies explore neural network-based classification techniques using language models such as RoBERTa fine-tuned for specific human- and AI-generated text mixtures, achieving greater accuracy than human responses [5; 6]. In white-box detection, identification strategies use watermarking, encoding hidden messages or identifiers in text to monitor suspicious or unauthorized activities [1]. These watermarks must be effectively embedded, verifiable, discreet, and resistant to removal through common text modification techniques, such as synonym replacement. Two main watermarking methods are post hoc and inference-time watermarking. Post hoc watermark ing embeds identifiers in pre-generated text, while inference-time watermarking modifies the text generation process to embed watermarks during decoding [1]. A notable study in this field [7] focuses on tools for detecting AI-generated texts in academic publications, highlighting the potential risks of unfair use of such content in academic settings. The authors emphasize that existing detection tools are neither accurate nor reliable, often misclassifying AI-generated texts as human-written. They propose the implementation of a prototype to detect AI-generated texts, comparing their solution with existing tools such as the OpenAI Text Classifier and ZeroGPT. The proposed model returns a percentage probability that indicates whether a text was generated by AI. It includes a user interface, a text detection tool, a survey for user feedback, and a connected database. The model was trained on a dataset comprising sections and paragraphs from research papers on Google Scholar, manually classified as human written or AI generated using ChatGPT. The dataset consisted of 1200 records, labeled 0 for human-written and 1 for AI-generated text. The authors used a word embedding technique based on simple frequencies for numerical text representation, followed by training an artificial neural network. They compared the accuracy of their model with the OpenAI and ZeroGPT classifiers. The OpenAI classifier provided results in the form of five phrases that indicated the likelihood of AI generation. The phrases'very unlikely' and 'unlikely' indicated that the text was considered human written, while 'possibly' and'very likely' indicated AI generation. For some records, the phrase 'unclear' was displayed, indicating ambiguous results. The OpenAI classifier required a minimum of 1000 characters to return a result and achieved an accuracy of 42.08%, with 40% of the results being unclear or not meeting the character requirement.