NLP needs Diversity outside of 'Diversity'

2026-04-15

AI Summary

This study addresses the narrow focus on fairness in current diversity research within natural language processing (NLP), which has overlooked inclusivity challenges across other subfields and hindered participation by marginalized researchers. For the first time, it systematically analyzes demographic data of researchers across NLP subfields, integrating social science methodologies, literature review, and subfield categorization to uncover how structural incentives, biases, and institutional barriers constrain multidimensional diversity. Moving beyond a limited conception of fairness, this work broadens the discourse to encompass field-wide inclusivity and proposes actionable strategies—such as disrupting inequitable feedback loops and removing geographic and linguistic barriers—to foster a more equitable and representative NLP research ecosystem, offering both empirical evidence and policy recommendations.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• diversity
• natural language processing
• fairness
• inclusion
• marginalized researchers

Innovation

• diversity in NLP
• researcher demographics
• geographical barriers
• linguistic inclusion
• equity in AI