Reducing Large Language Model Bias with Emphasis on 'Restricted
  Industries': Automated Dataset Augmentation and Prejudice Quantification
Devam Mondal Carlo Lipizzi
Despite the growing capabilities of large language models, there exists
concerns about the biases they develop. In this paper, we propose a novel,
automated mechanism for debiasing through specified dataset augmentation in the
lens of bias producers and in the context of 'restricted industries' with
limited data. We additionally create two new additional metrics, the mb-index
and db-index, to quantify bias, considering the idea that bias occurs due to
both intrinsic model architecture and dataset.