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Evaluating Inclusivity, Equity, and Accessibility of NLP Technology: A Case Study for Indian Languages

Simran Khanuja
Sebastian Ruder
Partha Talukdar
Findings of the Association for Computational Linguistics: EACL 2023

Abstract

In order for NLP technology to be widely applicable and useful, it needs to be **inclusive** of users across the world's languages, **equitable**, i.e., not unduly biased towards any particular language, and **accessible** to users, particularly in low-resource settings where compute constraints are common. In this paper, we propose an evaluation paradigm that assesses NLP technologies across all three dimensions, hence quantifying the diversity of users they can serve. While inclusion and accessibility have received attention in recent literature, quantifying equity is relatively unexplored. We propose to address this gap using the *Gini coefficient*, a well-established metric used for estimating societal wealth inequality. Using our paradigm, we highlight the distressed state of utility and equity of current technologies for Indian (IN) languages. Our focus on IN is motivated by their linguistic diversity and their large, varied speaker population. To improve upon these metrics, we demonstrate the importance of region-specific choices in model building and dataset creation and also propose a novel approach to optimal resource allocation in pursuit of building linguistically diverse, equitable technologies.