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In order for NLP technology to be widely applicable, fair, and useful, itneeds to serve a diverse set of speakers across the world's languages, beequitable, i.e., not unduly biased towards any particular language, and beinclusive of all users, particularly in low-resource settings where computeconstraints are common. In this paper, we propose an evaluation paradigm thatassesses NLP technologies across all three dimensions. While diversity andinclusion have received attention in recent literature, equity is currentlyunexplored. We propose to address this gap using the Gini coefficient, awell-established metric used for estimating societal wealth inequality. Usingour paradigm, we highlight the distressed state of current technologies forIndian (IN) languages (a linguistically large and diverse set, with a variedspeaker population), across all three dimensions. To improve upon thesemetrics, we demonstrate the importance of region-specific choices in modelbuilding 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 communityto employ multi-faceted evaluation when building linguistically diverse andequitable technologies.

Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages