Deep Learning for Bias Detection: From Inception to Deployment

TL;DR

This paper introduces a transfer learning-based deep learning model for automatically detecting unconscious bias in enterprise content, aiming to promote workplace inclusivity.

Contribution

It presents a novel approach combining pretraining on Wikipedia and fine-tuning on enterprise data for bias detection in corporate environments.

Findings

Model achieves high accuracy on independent bias datasets

Effective transfer learning improves bias detection performance

Deployment in real-world enterprise setting demonstrated feasibility

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.