A Survey of Race, Racism, and Anti-Racism in NLP (2021)

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.

## introduction race and language are tied in complicated ways. raciolinguistics scholars have studied how they are mutually constructed: historically, colonial powers construct linguistic and racial hierarchies to justify violence, and currently, beliefs about the inferiority of racialized people's language practices continue to justify social and economic exclusion @xcite . 1 furthermore, language is the primary means through which stereotypes and prejudices are communicated and perpetuated @xcite . however, questions of race and racial bias have been minimally explored in nlp literature. while researchers and activists have increasingly drawn attention to racism in computer science and academia, frequently-cited examples of racial bias in ai are often drawn from disciplines other than nlp, such as computer vision (facial recognition) @xcite or machine learning (recidivism risk prediction) @xcite . even the presence of racial biases in search engines like google @xcite has prompted little investigation in the acl community. work on nlp and race remains sparse, particularly in contrast to concerns about gender bias, which have led to surveys, workshops, and shared tasks @xcite . in this work, we conduct a comprehensive survey of how nlp literature and research practices engage with race. we first examine 79 papers from the acl anthology that mention the words 'race', 'racial', or 'racism' and highlight examples of how racial biases manifest at all stages of nlp model pipelines ( §3). we then describe some of the limitations of current work ( §4), specifically showing that nlp research has only examined race in a narrow range of tasks with limited or no social context. finally, in §5, we revisit the nlp pipeline with a focus on how people generate data, build models, and are affected by deployed systems, and we highlight current failures to engage with people traditionally underrepresented in stem and academia. while little work has examined the role of race in nlp specifically, prior work has discussed race in related fields, including human-computer interaction (hci) @xcite @xcite , fairness in machine learning @xcite , and linguistics @xcite . we draw comparisons and guidance from this work and show its relevance to nlp research. our work differs from nlp-focused related work on gender bias @xcite , 'bias' generally @xcite , and the adverse impacts of language models @xcite in its explicit focus on race and racism. in surveying research in nlp and related fields, we ultimately find that nlp systems and research practices produce differences along racialized lines. our work calls for nlp researchers to consider the social hierarchies upheld and exacerbated by nlp research and to shift the field toward "greater inclusion and racial justice" @xcite . it has been widely accepted by social scientists that race is a social construct, meaning it "was brought into existence or shaped by historical events, social forces, political power, and/or colonial conquest" rather than reflecting biological or 'natural' differences @xcite . more recent work has criticized the "social construction" theory as circular and rooted in academic discourse, and instead referred to race as "colonial constituted practices", including "an inherited western, modern-colonial practice of violence, assemblage, superordination, exploitation and segregation" @xcite . the term race is also multi-dimensional and can refer to a variety of different perspectives, including racial identity (how you self-identify), observed race (the race others perceive you to be), and reflected race (the race you believe others perceive you to be) @xcite @xcite . racial categorizations often differ across dimensions and depend on the defined categorization schema. for example, the united states census considers hispanic an ethnicity, not a race, but surveys suggest that 2/3 of people who identify as hispanic consider it a part of their racial background. 2 similarly, the census does not consider 'jewish' a race, but some nlp work considers anti-semitism a form of racism @xcite . race depends on historical and social context-there are no 'ground truth' labels or categories @xcite . as the work we survey primarily focuses on the united states, our analysis similarly focuses on the u.s. however, as race and racism are global constructs, some aspects of our analysis are applicable to other contexts. we suggest that future studies on racialization in nlp ground their analysis in the appropriate geo-cultural context, which may result 3 survey of nlp literature on race ## limitations in where and how nlp operationalizes race while §3 demonstrates ways that nlp systems encode racial biases, we next identify gaps and limitations in how these works have examined racism, focusing on how and in what tasks researchers have considered race. we ultimately conclude that prior nlp literature has marginalized research on race and encourage deeper engagement with other fields, critical views of simplified classification schema, and broader application scope in future work @xcite . ## nlp propagates marginalization of racialized people while in §4 we primarily discuss race as a topic or a construct, in this section, we consider the role, or more pointedly, the absence, of traditionally underrepresented people in nlp research. ## discussion we conclude by synthesizing some of the observations made in the preceding sections into more actionable items. first, nlp research needs to explicitly incorporate race. we quote @xcite : "[technical systems and social codes] operate within powerful systems of meaning that render some things visible, others invisible, and create a vast array of distortions and dangers." in the context of nlp research, this philosophy implies that all technology we build works in service of some ideas or relations, either by upholding them or dismantling them. any research that is not actively combating prevalent social systems like racism risks perpetuating or exacerbating them. our work identifies several ways in which nlp research upholds racism: nevertheless, none of these challenges can be addressed without direct engagement with marginalized communities of color. nlp researchers can draw on precedents for this type of engagement from other fields, such as participatory design and value sensitive design models @xcite . additionally, numerous organizations already exist that serve as starting points for partnerships, such as black in ai, masakhane, data for black lives, and the algorithmic justice league. finally, race and language are complicated, and while readers may look for clearer recommendations, no one data set, model, or set of guidelines can 'solve' racism in nlp. for instance, while we draw from linguistics, @xcite in turn call on linguists to draw models of racial justice from anthropology, sociology, and psychology. relatedly, there are numerous racialized effects that nlp research can have that we do not address in this work; for example, @xcite and @xcite discuss the environmental costs of training large language models, and how global warming disproportionately affects marginalized communities. we suggest that readers use our work as one starting point for bringing inclusion and racial justice into nlp. ## ethical considerations we, the authors of this work, are situated in the cultural contexts of the united states of america and the united kingdom/europe, and some of us identify as people of color. we all identify as nlp researchers, and we acknowledge that we are situated within the traditionally exclusionary practices of academic research. these perspectives have impacted our work, and there are viewpoints outside of our institutions and experiences that our work may not fully represent. isobelle clarke and jack grieve. 2017. dimensions of abusive language on twitter. in proceedings of the first workshop on abusive language online, pages 1-10, vancouver, bc, canada. association for computational linguistics. kimberlé crenshaw. 1989. demarginalizing the intersection of race and sex: a black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. university of chicago legal forum, 1989(8). thomas davidson, debasmita bhattacharya, and ingmar weber. 2019. racial bias in hate speech and abusive language detection datasets. in proceedings of the third workshop on abusive language online, pages 25-35, florence, italy. association for computational linguistics. maria de-arteaga, alexey romanov, hanna wallach, jennifer chayes, christian borgs, alexandra chouldechova, sahin geyik, krishnaram kenthapadi, and adam tauman kalai. 2019. bias in bios: a case study of semantic representation bias in a high-stakes setting. in proceedings of the conference on fairness, accountability, and transparency, page 120-128, new york, ny, usa. association for computing machinery. dorottya demszky, nikhil garg, rob voigt, james zou, jesse shapiro, matthew gentzkow, and dan jurafsky. 2019. analyzing polarization in social media: method and application to tweets on 21 mass shootings. in proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 2970-3005, minneapolis, minnesota. association for computational linguistics. lucas dixon, john li, jeffrey sorensen, nithum thain, and lucy vasserman. 2018. measuring and mitigating unintended bias in text classification. in proceedings of the 2018 aaai/acm conference on ai, ethics, and society, page 67-73, new york, ny, usa. association for computing machinery. jacob eisenstein, noah a. smith, and eric p. xing. 2011. discovering sociolinguistic associations with structured sparsity. in proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 1365-1374, portland, oregon, usa. association for computational linguistics. yanai elazar and yoav goldberg. 2018. adversarial removal of demographic attributes from text data. in proceedings of the 2018 conference on empirical methods in natural language processing, pages 11-21, brussels, belgium. association for computational linguistics. anjalie field, chan young park, and yulia tsvetkov. 2021. controlled analyses of social biases in wikipedia bios. computing research repository, arxiv:2101.00078. version 1. batya friedman, peter kahn, alan borning, and alina huldtgren. 2013. value sensitive design and information systems. in neelke doorn, daan schuurbiers, ibo van de poel, and michael gorman, editors, early engagement and new technologies: opening up the laboratory, volume 16. springer, dordrecht. roland g fryer jr and steven d levitt. 2004. the causes and consequences of distinctively black names. the quarterly journal of economics, 119(3):767-805. ryan j. gallagher, kyle reing, david kale, and greg ver steeg. 2017. anchored correlation explanation: topic modeling with minimal domain knowledge. transactions of the association for computational linguistics, 5:529-542. nikhil garg, londa schiebinger, dan jurafsky, and james zou. 2018. word embeddings quantify 100 years of gender and ethnic stereotypes. proceedings of the national academy of sciences, 115(16):e3635-e3644. ona de gibert, naiara perez, aitor garcía-pablos, and montse cuadros. 2018. hate speech dataset from a white supremacy forum. in proceedings of the 2nd workshop on abusive language online (alw2), pages 11-20, brussels, belgium. association for computational linguistics. nabeel gillani and roger levy. 2019. simple dynamic word embeddings for mapping perceptions in the public sphere. in proceedings of the third workshop on natural language processing and computational social science, pages 94-99, minneapolis, minnesota. association for computational linguistics. anthony g greenwald, debbie e mcghee, and jordan lk schwartz. 1998. measuring individual differences in implicit cognition: the implicit association test. journal of personality and social psychology, 74(6):1464. sophie groenwold, lily ou, aesha parekh, samhita honnavalli, sharon levy, diba mirza, and william yang wang. 2020. investigating african-american vernacular english in transformer-based text generation. in proceedings of the 2020 conference on empirical methods in natural language processing (emnlp), pages 5877-5883, online. association for computational linguistics. itika gupta, barbara di eugenio, devika salunke, andrew boyd, paula allen-meares, carolyn dickens, and olga garcia. 2020. heart failure education of african american and hispanic/latino patients: data collection and analysis. in proceedings of the first workshop on natural language processing for medical conversations, pages 41-46, online. association for computational linguistics. david l hamilton and tina k trolier. 1986. stereotypes and stereotyping: an overview of the cogni-tive approach. in j. f. dovidiom and s. l. gaertner, editors, prejudice, discrimination, and racism, pages 127--163. academic press. alex hanna, emily denton, andrew smart, and jamila smith-loud. 2020. towards a critical race methodology in algorithmic fairness. in proceedings of the 2020 conference on fairness, accountability, and transparency, page 501-512, new york, ny, usa. association for computing machinery. mohammed hasanuzzaman, gaël dias, and andy way. 2017. demographic word embeddings for racism detection on twitter. in proceedings of the eighth international joint conference on natural language processing (volume 1: long papers), pages 926-936, taipei, taiwan. asian federation of natural language processing. aurélie herbelot, eva von redecker, and johanna müller. 2012. distributional techniques for philosophical enquiry. in proceedings of the 6th workshop on language technology for cultural heritage, social sciences, and humanities, pages 45-54, avignon, france. association for computational linguistics. xiaolei huang, linzi xing, franck dernoncourt, and michael j. paul. 2020. multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition. in proceedings of the 12th language resources and evaluation conference, pages 1440-1448, marseille, france. european language resources association. anne h. charity hudley. 2017. language and racialization. in ofelia garcía, nelson flores, and massimiliano spotti, editors, the oxford handbook of language and society, pages 381-402. oxford university press. anne h charity hudley, christine mallinson, and mary bucholtz. 2020. toward racial justice in linguistics: interdisciplinary insights into theorizing race in the discipline and diversifying the profession. language, 96(4):e200-e235. ben hutchinson, vinodkumar prabhakaran, emily denton, kellie webster, yu zhong, and stephen denuyl. 2020. social biases in nlp models as barriers for persons with disabilities. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 5491-5501, online. association for computational linguistics. may jiang and christiane fellbaum. 2020. interdependencies of gender and race in contextualized word embeddings. in proceedings of the second workshop on gender bias in natural language processing, pages 17-25, barcelona, spain (online). association for computational linguistics. kenneth joseph and jonathan morgan. 2020. when do word embeddings accurately reflect surveys on our beliefs about people? in proceedings of the 58th annual meeting of the association for computational linguistics, pages 4392-4415, online. association for computational linguistics. pratik joshi, sebastin santy, amar budhiraja, kalika bali, and monojit choudhury. 2020. the state and fate of linguistic diversity and inclusion in the nlp world. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 6282-6293, online. association for computational linguistics. david jurgens, libby hemphill, and eshwar chandrasekharan. 2019. a just and comprehensive strategy for using nlp to address online abuse. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 3658-3666, florence, italy. association for computational linguistics. saket karve, lyle ungar, and joão sedoc. 2019. conceptor debiasing of word representations evaluated on weat. in proceedings of the first workshop on gender bias in natural language processing, pages 40-48, florence, italy. association for computational linguistics. anna kasunic and geoff kaufman. 2018. learning to listen: critically considering the role of ai in human storytelling and character creation. in proceedings of the first workshop on storytelling, pages 1-13, new orleans, louisiana. association for computational linguistics. brendan kennedy, xisen jin, aida mostafazadeh davani, morteza dehghani, and xiang ren. 2020. contextualizing hate speech classifiers with post-hoc explanation. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 5435-5442, online. association for computational linguistics. sharese king. 2020. from african american vernacular english to african american language: rethinking the study of race and language in african americans' speech. annual review of linguistics, 6(1):285-300. svetlana kiritchenko and saif mohammad. 2018. examining gender and race bias in two hundred sentiment analysis systems. in proceedings of the seventh joint conference on lexical and computational semantics, pages 43-53, new orleans, louisiana. association for computational linguistics. bogdan kulynych, david madras, smitha milli, inioluwa deborah raji, angela zhou, and richard zemel. 2020. participatory approaches to machine learning. international conference on machine learning workshop. sachin kumar, antonios anastasopoulos, shuly wintner, and yulia tsvetkov. 2021. machine translation into low-resource language varieties. in proceedings of the 59th annual meeting of the association in natural language processing and the 9th international joint conference on natural language processing (emnlp-ijcnlp), pages 6355-6361, hong kong, china. association for computational linguistics. emiel van miltenburg, desmond elliott, and piek vossen. 2017. cross-linguistic differences and similarities in image descriptions. in proceedings of the 10th international conference on natural language generation, pages 21-30, santiago de compostela, spain. association for computational linguistics. ehsan mohammady and aron culotta. 2014. using county demographics to infer attributes of twitter users. in proceedings of the joint workshop on social dynamics and personal attributes in social media, pages 7-16, baltimore, maryland. association for computational linguistics. aida mostafazadeh davani, leigh yeh, mohammad atari, brendan kennedy, gwenyth portillo wightman, elaine gonzalez, natalie delong, rhea bhatia, arineh mirinjian, xiang ren, and morteza dehghani. 2019. reporting the unreported: event extraction for analyzing the local representation of hate crimes. in proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (emnlp-ijcnlp), pages 5753-5757, hong kong, china. association for computational linguistics. 0 5 10 15 20 25 2 0 0 6 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 6 2 0 1 7 2 0 1 8 2 0 1 9 2 0 2 0 10 20 30 40 workshop acl emnlp naacl coling lrec *sem inlg conll ijcnlp ranlp tacl figure 2: venue of publication of 79 papers that mention "racial" or "racism". about half (46.8%) were published in workshops.