ArXiv Preprint
Recent advances and applications of language technology and artificial
intelligence have enabled much success across multiple domains like law,
medical and mental health. AI-based Language Models, like Judgement Prediction,
have recently been proposed for the legal sector. However, these models are
strife with encoded social biases picked up from the training data. While bias
and fairness have been studied across NLP, most studies primarily locate
themselves within a Western context. In this work, we present an initial
investigation of fairness from the Indian perspective in the legal domain. We
highlight the propagation of learnt algorithmic biases in the bail prediction
task for models trained on Hindi legal documents. We evaluate the fairness gap
using demographic parity and show that a decision tree model trained for the
bail prediction task has an overall fairness disparity of 0.237 between input
features associated with Hindus and Muslims. Additionally, we highlight the
need for further research and studies in the avenues of fairness/bias in
applying AI in the legal sector with a specific focus on the Indian context.
Sahil Girhepuje, Anmol Goel, Gokul Krishnan, Shreya Goyal, Satyendra Pandey, Ponnurangam Kumaraguru, Balaram Ravindran
2023-03-13
