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.

翻译：机器学习和人工智能领域取得了显著突破，尤其在自然语言处理与深度学习方面。随着社交媒体使用量的增加，对话中的仇恨言论检测已成为自然语言处理研究者关注的热点。然而，近期趋势表明，人们对人工智能模型的可解释性与可理解性维度的需求已获得深刻认知。基于上述因素，本研究旨在弥合仇恨言论预测与系统为支撑决策而生成的解释之间的鸿沟。这一目标通过以下方式实现：首先预测文本分类结果，随后提供一种事后、模型无关且基于替代模型的可解释性方法，以实现可解释性并防止模型偏差。由于双向Transformer模型BERT相较于其他机器学习模型具有最先进的效率，我们使用其进行预测。模型无关算法LIME为训练好的分类器输出生成解释，并预测影响模型决策的特征。通过人工评估模型生成的预测结果，经过全面评估后，我们观察到该模型在预测和解释其预测方面表现高效。最后，我们为拓展该研究提出了进一步方向。