NLP Needs Diversity outside of ‘Diversity’ (2025)

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.

## introduction natural language processing, like many subfields of computer science, has long been male-dominated, and subject to severe weird (western, educated, industrialized, rich, and developed) biases @xcite . as a science studying the fundamentally social phenomenon of language, it is imperative that the researchers working in nlp reflect the world it serves. recently, the diversity of the field of nlp has been reported by many sources to be increasing, including more women and people from varied geographic backgrounds @xcite . in the last five years, the acl has introduced multiple initiatives aimed at improving the fairness and inclusivity of the field, including adding an new diversity & inclusion committee in 2020 @xcite . however, we find that these gains are in fact not as vast as they appear-that diversity in nlp is largely concentrated in areas such as fairness and ethics, and have largely not penetrated "core" nlp fields. we perform novel research to analyze the current state of diversity in various topics within nlp, and speculate as to why many areas have been left behind in terms of inclusion. we synthesize our research to create a set of recommenda-* paper was written prior to employment by amazon tions for both improving the true diversity of nlp, and for accurately reporting diversity statistics. ## nlp's diversity by topic facing a gap in the research, we were motivated to perform a small experiment to understand how diversity in nlp varies by topic. ## explaining the diversity gap on its own, a gap in diversity in fairness-related fields is unsurprising. as a form of self-selection bias, it is understandable that those impacted the most by the lack of diversity in nlp would be disproportionately predisposed to join topics in fairness. however, we believe that there is substantial evidence of structural issues that pervade nlp which deepen this divide. cultural and institutional climates of different subfields may contribute to the observed disparities. studies have shown that women and researchers from marginalized communities often face higher barriers in stem, including biases in work distribution and collaboration opportunities @xcite . fairness-related fields may provide a comparatively more inclusive environment due to the nature of the research itself, which prioritizes diversity, equity, and inclusion. this could lead to a feedback loop where underrepresented researchers feel more welcome and supported in fairness-related subfields, reinforcing the diversity gap in other areas. the presence of role models is a key factor in guiding students into research areas @xcite . in particular, students may look to role models of their gender or specific minority group as influences in their decisions @xcite . it is possible that on top of self-selection bias, that incoming nlp students are pushed towards fairness by the presence of more like role models in the field already. in this sense, the diversity in fairness is self-sustaining, while the relative lack of diversity in other fields may be a vicious cycle. additionally, faculty in a field may express homophily, a preference (implicit or explicit) towards potential students in similar groups as them @xcite . for instance, a male professor may favor male graduate students. this narrows the entry pathways for underrepresented future researchers in fields with lower diversity. funding and hiring structures could also help create this disparity. "big tech" companies such as google, amazon, and microsoft are common sponsors and sources of funding for machine learning research, in-creasingly so in recent years @xcite . work sponsored by these companies is more likely to contain ethical statements and discuss potential negative outcomes, such as negative fairness outcomes @xcite . meanwhile, ding finds that top female researchers are more prominent at these large tech companies versus universities, which they attribute to a difference in hiring standards @xcite . taken together, this may create an environment where female researchers (and possibly researchers from other underrepresented categories) are more incentivized to join fairness research. however, more direct research on this topic would be needed to definitively prove such a proposition. there are also fundamental technological issues which prevent some researchers from properly engaging with many "core nlp" areas. linguistic diversity in language technologies is a greatly-studied and pervasive issue; as few as 7 languages are widely supported across datasets and models, with english dominating by a large margin @xcite . without models or datasets that support a researcher's native language, many downstream research tasks become close to impossible. this could de facto move many researchers into the fairness-adjacent area of linguistic diversity. ## recommendations we make a set of recommendations from our results. they highlight where progress has been made and where further action is needed to ensure diversity is not confined to fairness-focused research areas. ## conclusion the diversity gap between fairness and non-fairness fields in nlp is, in one sense, a failure-marginalized researchers have been pushed out of some areas and concentrated in others. but it is also a success story: fairness has become a subfield where diversity thrives. this likely reflects both exclusion from other areas and the welcoming climate within fairness work. having diverse researchers study diversity is not inherently an issue, nor is the uneven distribution of diversity necessarily problematic. however, diversity is equally critical in non-fairness subfields, not only as an ethical imperative but because it demonstrably improves research quality, with mixed-gender authorship often outperforming single-gender teams @xcite . blanket-level diversity statistics obscure these imbalances, and true inclusion requires deeper reflection on where inclusivity has taken hold-and where it still has not.