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

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, equity is currently 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 diversity of current technologies for Indian (IN) languages, motivated by their linguistic diversity and 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 during fine-tuning. Finally, we discuss steps that must be taken to mitigate these biases and call upon the community to incorporate our evaluation paradigm when building linguistically diverse technologies.