NLP needs Diversity outside of 'Diversity'

TL;DR

This paper highlights the need for greater diversity in NLP research beyond fairness, emphasizing barriers faced by marginalized researchers and proposing strategies for inclusivity across all NLP subfields.

Contribution

It identifies biases and barriers limiting diversity in NLP, especially outside fairness, and offers recommendations to foster a more inclusive and equitable research community.

Findings

NLP diversity is concentrated mainly in fairness-related areas.

Marginalized researchers face barriers due to biases and incentives.

Addressing geographical and linguistic barriers can improve inclusivity.

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.