Distinguishing Human-Written and AI-Generated Text: A: Comprehensive Study Using Explainable Artificial Intelligence in Text Classification

Enhancing interpretability without compromising accuracy is a critical challenge in text classification. This research explores the integration of Explainable Artificial Intelligence (XAI) techniques with advanced machine learning models, utilizing the Local Interpretable Model-Agnostic Explanations (LIME) framework to provide transparency. A fine-tuned BERT model achieved state-of-the-art performance, surpassing Random Forest and Sentence Embedding-based models with a perfect 100% accuracy (ROC-AUC score of 1.00). While Random Forest classifiers offered a solid baseline, they struggled with semantic nuances, underscoring the need for embedding-based approaches. The study highlights the inherent trade-off between interpretability and accuracy, demonstrating that while transformer-based models like BERT excel at capturing complex linguistic patterns, their "black-box" nature necessitates tools like LIME for explainability. By bridging this gap, the research contributes to the development of more transparent, reliable, and high-performing AI systems.