A Survey of Race, Racism, and Anti-Racism in NLP

TL;DR

This survey reviews 79 NLP papers to highlight how race and racism are addressed, revealing biases, gaps, and the need for inclusive, justice-oriented research practices in NLP.

Contribution

It systematically analyzes race-related considerations in NLP literature, exposing biases, gaps, and advocating for more inclusive and justice-focused NLP research.

Findings

Race is often overlooked in NLP tasks.

Most work treats race as a fixed, single-dimensional variable.

Marginalized voices are nearly absent in NLP literature.

Abstract

Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.