Deep Learning for Bias Detection: From Inception to Deployment

• URL: http://arxiv.org/abs/2110.15728v1
• Date: Tue, 12 Oct 2021 13:57:54 GMT
• Title: Deep Learning for Bias Detection: From Inception to Deployment
• Authors: Md Abul Bashar, Richi Nayak, Anjor Kothare, Vishal Sharma, Kesavan Kandadai
• Abstract summary: 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. The trained model is thoroughly evaluated on independent datasets to ensure a general application.
• Score: 4.51073220028236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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