  There have been remarkable breakthroughs in Machine Learning and Artificial
Intelligence, notably in the areas of Natural Language Processing and Deep
Learning. Additionally, hate speech detection in dialogues has been gaining
popularity among Natural Language Processing researchers with the increased use
of social media. However, as evidenced by the recent trends, the need for the
dimensions of explainability and interpretability in AI models has been deeply
realised. Taking note of the factors above, the research goal of this paper is
to bridge the gap between hate speech prediction and the explanations generated
by the system to support its decision. This has been achieved by first
predicting the classification of a text and then providing a posthoc, model
agnostic and surrogate interpretability approach for explainability and to
prevent model bias. The bidirectional transformer model BERT has been used for
prediction because of its state of the art efficiency over other Machine
Learning models. The model agnostic algorithm LIME generates explanations for
the output of a trained classifier and predicts the features that influence the
model decision. The predictions generated from the model were evaluated
manually, and after thorough evaluation, we observed that the model performs
efficiently in predicting and explaining its prediction. Lastly, we suggest
further directions for the expansion of the provided research work.
