mlsanityData to 7 Oct 2026

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

arXiv 2405.17159 ↗First posted 27 May 2024cs.CL cs.CY cs.HCcited by 22

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Abstract (arXiv)

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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 37 of its 126 references that are in the map. First posted on arXiv 27 May 2024.

Builds on

37 of 126 references.

References · 37

  1. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  2. 2018Datasheets for Datasetscited by 1,371
  3. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  5. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 346
  6. 2019Measurement and Fairnesscited by 299
  7. 2020Measuring and Reducing Gendered Correlations in Pre-trained Modelscited by 222
  8. 2021The Values Encoded in Machine Learning Researchcited by 172
  9. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  10. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
  11. 2019It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitutioncited by 102
  12. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
25 more
  1. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  2. 2019What's in a Name? Reducing Bias in Bios without Access to Protected Attributescited by 51
  3. 2018Racial categories in machine learningcited by 48
  4. 2022Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gendercited by 47
  5. 2020"You are grounded!": Latent Name Artifacts in Pre-trained Language Modelscited by 39
  6. 2021Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Modelscited by 39
  7. 2021Disembodied Machine Learning: On the Illusion of Objectivity in NLPcited by 37
  8. 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairnesscited by 26
  9. 2021Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelscited by 23
  10. 2022SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Modelscited by 22
  11. 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fieldscited by 20
  12. 2020Against Scale: Provocations and Resistances to Scale Thinkingcited by 18
  13. 2020Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citationscited by 17
  14. 2024Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigationcited by 17
  15. 2023Values, Ethics, Morals? On the Use of Moral Concepts in NLP Researchcited by 13
  16. 2021The Contestation of Tech Ethics: A Sociotechnical Approach to Technology Ethics in Practicecited by 12
  17. 2024Classist Tools: Social Class Correlates with Performance in NLPcited by 9
  18. 2023Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoningcited by 8
  19. 2019Human-Computer Insurrection: Notes on an Anarchist HCIcited by 7
  20. 2023Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translationcited by 7
  21. 2016Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methodscited by 6
  22. 2021Avoiding bias when inferring race using name-based approachescited by 6
  23. 2022An Open-Source Cultural Consensus Approach to Name-Based Gender Classificationcited by 5
  24. 2024Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter and What They Representcited by 5
  25. 2024ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages using Wikidatacited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 10; 2026: 10.

22 papers.

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  1. 2025The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Modelscited by 40
  2. 2024WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Casecited by 9
  3. 2026One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalizationcited by 9
  4. 2025No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomycited by 8
  5. 2025Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMscited by 7
  6. 2025Agree to Disagree? A Meta-Evaluation of LLM Misgenderingcited by 6
  7. 2025On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisionscited by 5
  8. 2025On the Mutual Influence of Gender and Occupation in LLM Representationscited by 4
  9. 2025Can Large Language Models Really Recognize Your Name?cited by 4
  10. 2025Sports and Women's Sports: Gender Bias in Text Generation with Olympic Datacited by 3
  11. 2026Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Biascited by 3
  12. 2024Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Modelscited by 1
10 more
  1. 2025Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contextscited by 1
  2. 2026Human-Centred LLM Privacy Audits: Findings and Frictionscited by 1
  3. 2026Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiringcited by 1
  4. 2025Teaching and Critiquing Conceptualization and Operationalization in NLPcited by 0
  5. 2026ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cuescited by 0
  6. 2026NLP needs Diversity outside of 'Diversity'cited by 0
  7. 2026Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmarkcited by 0
  8. 2026No PUN Intended: Plausible Unknown Names for Person-Centred LLM Evaluationcited by 0
  9. 2026WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ Identitiescited by 0
  10. 2026Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiringcited by 0