Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP (2024)

Abstract: Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in doing so. To guide future work that uses names and sociodemographic characteristics, we provide an overview of relevant research: first, we present an interdisciplinary background on names and naming. We then survey the issues inherent to associating names with sociodemographic attributes, covering problems of validity (e.g., systematic error, construct validity), as well as ethical concerns (e.g., harms, differential impact, cultural insensitivity). Finally, we provide guiding questions along with normative recommendations to avoid validity and ethical pitfalls when dealing with names and sociodemographic characteristics in natural language processing.

## introduction a person's identity is a complex and paradoxical thing -it simultaneously identifies someone's uniqueness, and categorizes them, identifying what they have in common with others @xcite . a perfect example of this phenomenon is a person's name. personal names are proper nouns used to refer to individuals. they play an important distinguishing role in our lives, as they let us uniquely represent people mentally, refer to them directly in speech, and underscore their significance as individuals @xcite . for these reasons, personal names are a linguistic universal, i.e., they appear across languages and cultures, although naming customs vary across the world @xcite . but alongside differentiating people, names also categorize them in their society. names assigned to validity issues ethical issues errors not robustly quantifiable systematic error poor construct validity systems of classification create results errors cause harms errors and harms not distributed equally representational harms cultural insensitivity no shifts in power people often index aspects of identity that are important in the context of their society, including sex, religion, tribe, stage of life, etc. personal names are thus rich resources to understand the social organization of communities, and have been studied across anthropology @xcite , sociology @xcite , linguistics @xcite , and onomastics @xcite . in natural language processing (nlp) as well, personal names have a long history of use-nlp researchers have worked on identifying and disambiguating uses of personal names @xcite @xcite and have examined name translation @xcite @xcite and name transliteration @xcite @xcite . increasingly, nlp researchers also use personal names along with sociodemographic characteristics for passive analysis of media and scholarly content @xcite @xcite , or to examine model biases and harms @xcite @xcite . however, these papers engage to varying degrees with concerns that have been raised outside of nlp about the methodological validity and ethics of associating names with sociodemographic characteristics. we argue that neglect of these issues is a significant barrier to valid and respectful research, as well as more inclusive nlp systems. hence, we contribute an overview of the issues with associating names with sociodemographic attributes (focused on gender and race, two popular categories used in nlp research), as shown in figure 1 . we begin with background on names in other fields and in nlp ( §2), and lay out the problems with validity ( §3) and ethical concerns ( §4) raised when associating personal names with sociodemographic characteristics. finally, we present guiding questions along with normative recommendations ( §5) to guide future work in the field with names. bias statement. we consider group-level, individual, and representational harms, terms which we explain where we use them in section 4. ## background we begin with some background: on names and naming, and on the use of names in nlp. ## validity issues in this section, we present issues of validity when associating names with sociodemographic categories, or using names to infer them. issues of validity mean that results with these operationalizations may neither be indicative of what we actually want to measure, nor of reality. error is not quantifiable without asking humans. the accuracy of using names to infer sociodemographic characteristics of individuals cannot be quantified without ground truth data, which for people's identities, can only be obtained by asking them. multiple studies thus empirically analyze the error rates of name-based gender and race inference systems as compared to gold data in different contexts @xcite @xcite . 1 for example, @xcite evaluate gender and race inference systems using self-reported data from nearly 20,000 individuals. importantly, their self-reported data does not directly transfer to other contexts, as their respondents are authors of english language social science journal articles who are mostly located in the u.s. using this data as reference data for a system with users located primarily in india, or for u.s. authors in a different century, makes little sense. in new environments, it is simply not possible to reasonably estimate the bounds of error of a name-based analysis, and results without a corresponding analysis of selfreported data should not be taken seriously. popular design choices lead to systematic error and selection bias. names that are uninformative of a sociodemographic characteristic present an issue for tools that aim to label everyone. in the context of gender, names like alex have no unique gendered association in the u.s. and canada; with race, names assigned by black and white parents overlap in the u.s. @xcite , and religious names are used around the world @xcite ; olúwáfe ḿi, 2014)); at the intersection of gender and race, many chinese names are not genderassociated when romanized, and infrequent names are also not informative. two common design choices for handling uninformative names are to assign the majority class label anyway, or, alternatively, to just exclude them. assigning the majority class (i.e., classifying all people named miaoran as female if a gender prediction tool predicts the name to be "60% female") results in systematic error @xcite . on the other hand, excluding uninformative names from the analysis completely alters the makeup of the data and therefore the results @xcite , resulting in selection bias. both choices affect internal validity, i.e., gaps in the translation from measurements to overall conclusions @xcite , leading to less robust and trustworthy results. poor construct validity. construct validity asks how well an abstract concept can be measured through some indicator @xcite ; in our case, the question is: how valid is it to assign sociodemographic categories via names? 2 the answer to this depends on what aspects of the sociodemographic category we are interested in: identity, socialization, expression, perception-all of which could differ and are frequently conflated @xcite . as discussed previously, many names are simply not informative of certain sociodemographic identities in given contexts and with homogeneous populations; @xcite find that overall error rates of name-based gender and race imputation tools range from 4.6% to 86% overall, and up to 100% for particular subgroups, depending on the tool. however, when it comes to the perception of names as indexing a sociodemographic category, some names may have stronger construct validity, an assumption used by @xcite in their examination of names assigned at birth that are strongly associated with the baby's sex and the parents' race/ethnicity. on the other hand, mohammad (2020) uses names to operationalize both identity (to investigate trends in authorship) and perception (to investigate trends in citation) in a bibliometric analysis of the acl anthology, even though these need not match, and many underrepresented names are uninformative of identity as well as perception @xcite . as names do not neatly line up with sociode-mographic identities, perceptions, or experiences in a context-independent way, it is critical to investigate construct validity of names in any setting where they are used. ## ethical issues the issues we have examined so far impact the scientific validity of claims made using personal names and sociodemographic categories. many of these problems arise from assumptions that can also be criticized on ethical grounds, as we show. ## guiding questions and recommendations in the previous sections, we have reviewed the myriad of issues surrounding the accuracy, validity and ethical use of names along with sociodemographic characteristics, and noted that all these issues arise from the same assumptions and inform each other. in addition, we have shown that these problems apply overwhelmingly to those who are not cisgender, white, normatively named in a western context, and well-represented in publicly available data. thus, work that uses names to operationalize people's sociodemographic categories most misrepresents and further marginalizes those who are already at the margins. we take the normative position that this is not acceptable collateral damage, even (and especially!) in the name of ostensible fairness. thus, we come up with guiding questions and recommendations for nlp practitioners who are considering the use of names as they relate to sociodemographic categories. these are summarized in what are you aiming to study-names? or people, via their names? it is acceptable to investigate what concepts nlp models associate with names, e.g., madeleine with kindness. it is even acceptable to demonstrate that nlp models associate marius with the pronoun he or with being male, and that these associations mirror common human associations @xcite . it is marginally acceptable to associate names with sociodemographic characteristics using imaginary people, e.g., drawing insights about gender bias more broadly based on how nlp models handle synthetic names of people assumed to be exclusively female; while doing so does not compromise people's autonomy and dignity, it does further entrench hegemonic folk theories of names and people's identities, which has cultural harms. finally, it is unacceptable to present results about real people based solely on the assumption that their names provide a reliable signal about their identities, e.g., nlp papers authored by people named madeleine and marius cannot on their own provide trustworthy insights into gender and racial representation in the field, unless those specific individuals are asked about their gender. what aspects of names are you interested in? names are rich objects of study with variation in form, length, training data frequency, tokenization, associations, the strengths of these associations, and more. 4 once you have decided what aspects to study, they must be operationalized and measured carefully, with attention to the context of the study or eventual system deployment. this includes the scope of what counts as a "name." for instance, considering the use of english common nouns as names (e.g., cloud) is particularly important when working with data from or systems deployed in china, where this naming practice is common @xcite . ensure that pre-processing choices are contextualized and do not distort results, that names are understood within context, and that error can be quantified robustly in the given context. thus, when measuring training data frequency of names, counting cloud tokens as names must consider when it is used as a name and when it is used simply as a noun. error could be quantified through manual analysis on a subset of the data. what aspects of people are you interested in? people's identity and perceptions of them can differ, and these shape their experiences in various ways. therefore, it is first necessary to decide which aspects are relevant for a study. attempting to infer someone's identity using names is simply unacceptable due to the range of methodological and ethical concerns we list in this paper. we echo onomastic advice from nearly 40 years ago (weitman, 1981), i.e., that "inferences from names must be to the givers of these names, not to their bearers. what is more, inferences must always be to sociological formations (such as social classes, ethnic groups, historical generations, and the like), not to individual name-givers." in addition to studying formations of name-givers, it can also be acceptable to study perceptions of identity based on names. for instance, numerous sociology papers have investigated racial and ethnic perceptions, as well as occupational stereotypes, based on names @xcite . again, we emphasize that perceptions based on names are also highly contextual and non-universal. what is your context? it is essential to understand the geographical, temporal, and cultural context of data with names, and document this information for datasets, e.g., with datasheets @xcite . what is the geographic, temporal, cultural and political context of the name data, namebearers, models and sociodemographic categories you use? who are the people who will be impacted by your work, and what is their context? what do you know about the naming practices in these contexts and the hetereogeneity in these practices? are you quantifying error with self-reported data? we posit that it is unacceptable to use names without deeply engaging with context in these senses, and stress that ascribing contemporary western identity categories to historical peoples without acknowledging the difference in contexts is reductive. is processing names with nlp systems necessary to answer your questions? for questions about human identity and perception based on names, nlp may not be the only or best method available. we warn against technical solutionism @xcite ; researchers should reflect on whether their questions could be approached with interviews, case studies, fourth-world paradigms, and so on @xcite . qualitative methods can provide deeper, richer evidence while respecting people's autonomy, dignity and context. if your questions are instead about nlp systems, then processing names with them is certainly necessary, but we note that methodological pluralism and interdisciplinarity can enrich our practice as nlp researchers and practitioners regardless @xcite . what kinds of harms apply? how can you mitigate them? our paper provides a starting point for harms that are relevant to the use of names and sociodemographic characteristics in nlp, and we encourage transparency about methodological and ethical problems @xcite . it is unacceptable to sideline these problems in the name of "social good" @xcite @xcite , and rather than treating entire segments of the world as limitations of or future work for your research, we encourage changing the methods themselves, as @xcite do with neopronouns. we recommend firmly grounding work in the ethical principles of autonomy, justice, and beneficence for people @xcite , which we note are sadly under-represented in machine learning research @xcite . are you describing or prescribing? descriptions of social phenomena are often conflated with normative behaviour (i.e., assumptions and assertions that create and reinforce norms) in nlp @xcite . this is the subtle but significant difference between showing that sociodemographic name associations in language models mirror the judgements of some group of humans, versus stating that model associations should mirror the judgements of some group of humans. the latter "cannot avoid creating and reinforcing norms" @xcite . therefore, researchers should clearly distinguish descriptive and normative behaviours in the design, execution, and presentation of their experiments @xcite . system designers do have to make decisions about how systems should behave, i.e., they need to choose to perpetuate harmful structures in service of usability or to impose their own values on users and stakeholders when they take an advocacy position. this is an ethical dilemma in design that participatory methods and feminist epistemologies are uniquely positioned to help with @xcite . how does your work reify or redistribute power? central to nlp and computer science at large are scale thinking @xcite , quantitative methodologies @xcite , and the illusion of objectivity @xcite . all these values serve to reify existing hierarchies and power structures. we must first recognize our own power as nlp researchers and practitioners, and how our work can reinforce infrastructure for (mis)classifying real people and enable surveillance and harms at scale. we recommend a counterpower stance @xcite , situated knowledges @xcite , and methods informed by a politic, e.g., intersectionality, a critical framework that centers justice, power, and reflexivity, and mandates praxis with teeth @xcite @xcite . particularly for those of us who are interested in using nlp for social good, we should constantly be asking: "social good for whom?" the differential impact on people matters, and as researchers and practitioners, we have a responsibility to attend to it and resist the othering perpetuated by classification systems. is it still worth it? after considering all these guiding questions, we remind the reader that refusal is possible @xcite @xcite , and indeed an important part of the history of science @xcite ; united states national commission for the protection of human subjects of @xcite . ## related work several papers study and critically interrogate the inference and use of sociodemographic information in computing @xcite @xcite @xcite @xcite , many of which touch upon names but do not address them in detail. the work that deals with names in particular are all outside of nlp: karimi et al. (2016); keyes (2017 ); tzioumis (2018); mihaljević et al. (2019); scheuerman et al. (2019); lockhart et al. (2023); van buskirk et al. (2023). these papers have different scopes and take a variety of positions with regards to the ethics of name-based inference, some of which we find insufficiently radical. finally, our recommendations echo those from prior work (particularly in the fields of human-computer interaction and science and technology studies), but are contextualized for names in nlp. among others, we take inspiration from keyes et al. (2019); hanna and park (2020); blodgett et al. (2020); scheuerman et al. (2020a); and green (2021). ## conclusion we present the field with an overview of names and naming as discussed in other disciplines. we lay out background on naming practices around the world and describe how these practices create issues of validity (e.g., selection bias and construct validity) and ethical concerns (e.g., harms, cultural insensitivity), that affect nlp uses of names and sociodemographic characteristics. finally, we present a list of guiding questions and normative suggestions towards addressing these concerns in future work involving names in nlp.