Enhancing Transparency and Interpretability in Toxic Comment Classification: A Study on the Integration of Explainable Artificial Intelligence (XAI) Techniques || Dr. Pallavi Devendra Tawde, Mr. Jadyn Dias

More than ever, robust, and interpretable toxic comment recognition methods are required to manage the growing frequency of toxic comments on online platforms. The research tries to incorporate techniques in Explainable Artificial Intelligence (XAI) to improve the transparency and comprehensibility of toxic comment classification. Using a comprehensive dataset, we designed a model architecture which includes the latest practices in XAI. Through rigorous experimentation, our study proves the usefulness of such methods as tools that not only increase classification accuracy but also illuminate model decision-making processes. One view is that by adding LIME and Eli5 to toxic comment classification, model performance improves both in terms of accuracy and interpretation for decisions. Our results provide valuable insights into the model's strengths and areas for refinement, contributing to the transparency and interpretability of toxic comment classification. This research contributes to the evolving landscape of interpretable machine learning, offering a pathway to more accountable and trustworthy toxic comment moderation systems. Keywords: Explainable artificial intelligence, Model interpretability, toxic comment classification, LIME, Eli5