Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages
This addresses the need for fair and inclusive NLP for diverse language speakers, especially in low-resource settings, though it is incremental in applying existing metrics to a new domain.
The paper tackles the problem of evaluating NLP technology for diversity, equity, and inclusion, particularly for Indian languages, and finds that current technologies are distressed across all three dimensions, with the Gini coefficient used to quantify inequity. It proposes a novel approach to optimal resource allocation during fine-tuning to improve these metrics.
In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world's languages, be equitable, i.e., not unduly biased towards any particular language, and be inclusive of all 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. While diversity and inclusion 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 current technologies for Indian (IN) languages (a linguistically large and diverse set, with a varied speaker population), across all three dimensions. To improve upon these metrics, we demonstrate the importance of region-specific choices in model building and dataset creation, and more importantly, propose a novel, generalisable approach to optimal resource allocation during fine-tuning. Finally, we discuss steps to mitigate these biases and encourage the community to employ multi-faceted evaluation when building linguistically diverse and equitable technologies.