Interpretability in Hate Speech and Offensive Language Detection: Leveraging Transformers with Explainable AI

Hate speech and offensive language are on the rise, and this is a serious problem as social media keeps developing into an essential tool for communication. This expanding issue highlights the need for more sophisticated and potent detection systems to build a safer and more welcoming online environment. Because hate speech is so diverse and includes insulting language based on factors such as gender, ethnicity, race, and religion, it can be challenging to identify. This research has investigated machine learning, deep learning, transfer learning, and using labeled datasets from Kaggle to increase accuracy. Even with advancements, issues like linguistic subtleties, erroneous definitions, a lack of data, and the requirement for more openness in classification nonetheless exist. Even though traditional models like SVM have shown respectable results, some models have performed better, such as RoBERTa, which achieved a 96% accuracy. Interpretability techniques, LIME, have been proposed to improve comprehension by increasing transparency in decision-making processes.