  With surge in online platforms, there has been an upsurge in the user
engagement on these platforms via comments and reactions. A large portion of
such textual comments are abusive, rude and offensive to the audience. With
machine learning systems in-place to check such comments coming onto platform,
biases present in the training data gets passed onto the classifier leading to
discrimination against a set of classes, religion and gender. In this work, we
evaluate different classifiers and feature to estimate the bias in these
classifiers along with their performance on downstream task of toxicity
classification. Results show that improvement in performance of automatic toxic
comment detection models is positively correlated to mitigating biases in these
models. In our work, LSTM with attention mechanism proved to be a better
modelling strategy than a CNN model. Further analysis shows that fasttext
embeddings is marginally preferable than glove embeddings on training models
for toxicity comment detection. Deeper analysis reveals the findings that such
automatic models are particularly biased to specific identity groups even
though the model has a high AUC score. Finally, in effort to mitigate bias in
toxicity detection models, a multi-task setup trained with auxiliary task of
toxicity sub-types proved to be useful leading to upto 0.26% (6% relative) gain
in AUC scores.
