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Online conversations can be toxic and subjected to threats, abuse, orharassment. To identify toxic text comments, several deep learning and machinelearning models have been proposed throughout the years. However, recentstudies demonstrate that because of the imbalances in the training data, somemodels are more likely to show unintended biases including gender bias andidentity bias. In this research, our aim is to detect toxic comment and reducethe unintended bias concerning identity features such as race, gender, sex,religion by fine-tuning an attention based model called BERT(BidirectionalEncoder Representation from Transformers). We apply weighted loss to addressthe issue of unbalanced data and compare the performance of a fine-tuned BERTmodel with a traditional Logistic Regression model in terms of classificationand bias minimization. The Logistic Regression model with the TFIDF vectorizerachieve 57.1% accuracy, and fine-tuned BERT model's accuracy is 89%. Code isavailable athttps://github.com/zim10/Determine_Toxic_comment_and_identity_bias.git

Determination of toxic comments and unintended model bias minimization using Deep learning approach