  To create a more inclusive workplace, enterprises are actively investing in
identifying and eliminating unconscious bias (e.g., gender, race, age,
disability, elitism and religion) across their various functions. We propose a
deep learning model with a transfer learning based language model to learn from
manually tagged documents for automatically identifying bias in enterprise
content. We first pretrain a deep learning-based language-model using
Wikipedia, then fine tune the model with a large unlabelled data set related
with various types of enterprise content. Finally, a linear layer followed by
softmax layer is added at the end of the language model and the model is
trained on a labelled bias dataset consisting of enterprise content. The
trained model is thoroughly evaluated on independent datasets to ensure a
general application. We present the proposed method and its deployment detail
in a real-world application.
