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.

本文针对语言技术和人工智能领域的进展和应用进行研究，探讨印度视角下法律领域中所存在的公平性问题，特别关注 AI 模型中的算法偏差对于对印度教和穆斯林的关联特征的差异性的影响，并强调了在印度的法律领域中，公平性 / 偏差的研究仍有待进一步的耕耘。