NLP needs Diversity outside of 'Diversity'

arXiv cs.CL / 4/17/2026

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Key Points

The paper argues that “diversity” efforts in NLP have mainly focused on fairness-adjacent topics, leaving other subfields behind.
It attributes this imbalance to incentives, biases, and structural barriers that either exclude marginalized researchers from non-fairness areas or push them toward fairness-related work.
The authors examine NLP researcher demographics by subfield to support their claims.
They propose recommendations aimed at improving inclusion and equity across all NLP areas, emphasizing breaking feedback loops that entrench disparities.
The paper also stresses addressing geographical and linguistic barriers that limit participation in NLP research.

Abstract

This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness. We further argue that this is the result of a number of incentives, biases, and barriers which come together to disenfranchise marginalized researchers in non-fairness fields, or to move them into fairness-related fields. We substantiate our claims with an investigation into the demographics of NLP researchers by subfield, using our research to support a number of recommendations for ensuring that all areas within NLP can become more inclusive and equitable. In particular, we highlight the importance of breaking down feedback loops that reinforce disparities, and the need to address geographical and linguistic barriers that hinder participation in NLP research.