Akhash Amarnath


2022

pdf bib
Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique
Aparna Garimella | Rada Mihalcea | Akhash Amarnath
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

BERT-like language models (LMs), when exposed to large unstructured datasets, are known to learn and sometimes even amplify the biases present in such data. These biases generally reflect social stereotypes with respect to gender, race, age, and others. In this paper, we analyze the variations in gender and racial biases in BERT, a large pre-trained LM, when exposed to different demographic groups. Specifically, we investigate the effect of fine-tuning BERT on text authored by historically disadvantaged demographic groups in comparison to that by advantaged groups. We show that simply by fine-tuning BERT-like LMs on text authored by certain demographic groups can result in the mitigation of social biases in these LMs against various target groups.

2021

pdf bib
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation
Aparna Garimella | Akhash Amarnath | Kiran Kumar | Akash Pramod Yalla | Anandhavelu N | Niyati Chhaya | Balaji Vasan Srinivasan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021