Detecting and Unmasking AI-Generated Texts through Explainable Artificial Intelligence using Stylistic Features

Abstract

In recent years, Artificial Intelligence (AI) has significantly transformed various aspects of human activities, including text composition. The advancements in AI technology have enabled computers to generate text that closely mimics human writing which is raising concerns about misinformation, identity theft, and security vulnerabilities. To address these challenges, understanding the underlying patterns of AI-generated text is essential. This research focuses on uncovering these patterns to establish ethical guidelines for distinguishing between AI-generated and human-generated text. This research contributes to the ongoing discourse on AI-generated content by elucidating methodologies for distinguishing between human and machinegenerated text. The research delves into parameters such as syllable count, word length, sentence structure, functional word usage, and punctuation ratios to detect AI-generated text. Furthermore, the research integrates Explainable AI (xAI) techniques—LIME and SHAP—to enhance the interpretability of machine learning model predictions. The model demonstrated excellent efficacy, showing an accuracy of 93%. Leveraging xAI techniques, further uncovering that pivotal attributes such as Herdan’s C, MaaS, and Simpson’s Index played a dominant role in the classification process.