Safeguarding Authenticity in Text with BERT-Powered Detection of AI-Generated Content
Utsho Chakraborty, Jaydeep Gheewala, Sheshang Degadwala, Dhairya Vyas, Mukesh Soni
Abstract
This research study explores the crucial domain of upholding textual authenticity by introducing a comprehensive method for identifying AI-generated content, employing BERT (Bidirectional Encoder Representations from Transformers). In a time when Artificial Intelligence (AI) significantly shapes written communication, it becomes imperative to differentiate between text produced by humans and that generated by machines. The proposed approach utilizes the capabilities of BERT by delving into contextual embedding, revealing complex patterns that serve as indicators of AI origin. Through meticulous experimentation and evaluation, we substantiate the effectiveness of our method in precisely discerning AI-generated text. This contribution adds to the ongoing endeavors to safeguard the integrity of human-authored content in the ever-evolving digital landscape.