# NLP needs Diversity outside of 'Diversity'

arXiv: https://arxiv.org/abs/2604.14595 · HTML: https://mlsanity.com/paper/2604.14595/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP](https://mlsanity.com/paper/2405.17159/) (2024)
- [Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages](https://mlsanity.com/paper/2205.12676/) (2022)
- [Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations](https://mlsanity.com/paper/2005.00962/) (2020)
- [A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data Sets](https://mlsanity.com/paper/2403.03909/) (2024)
- [Why AI Is WEIRD and Should Not Be This Way: Towards AI For Everyone, With Everyone, By Everyone](https://mlsanity.com/paper/2410.16315/) (2024)
- [Voices of Her: Analyzing Gender Differences in the AI Publication World](https://mlsanity.com/paper/2305.14597/) (2023)
- [Is academia becoming more localised? The growth of regional knowledge networks within international research collaboration](https://mlsanity.com/paper/2101.09520/) (2021)

## Abstract (arXiv)

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.