Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Bias Detection: From Inception to Deployment   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.

7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con