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

arXiv: https://arxiv.org/abs/2405.17159 · HTML: https://mlsanity.com/paper/2405.17159/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 126 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 30 of 37)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://mlsanity.com/paper/2010.06032/) (2020)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://mlsanity.com/paper/1909.00871/) (2019)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [What's in a Name? Reducing Bias in Bios without Access to Protected Attributes](https://mlsanity.com/paper/1904.05233/) (2019)
- [Racial categories in machine learning](https://mlsanity.com/paper/1811.11668/) (2018)
- [Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender](https://mlsanity.com/paper/2202.11923/) (2022)
- ["You are grounded!": Latent Name Artifacts in Pre-trained Language Models](https://mlsanity.com/paper/2004.03012/) (2020)
- [Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models](https://mlsanity.com/paper/2110.00672/) (2021)
- [Disembodied Machine Learning: On the Illusion of Objectivity in NLP](https://mlsanity.com/paper/2101.11974/) (2021)
- [Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness](https://mlsanity.com/paper/2303.17555/) (2023)
- [Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models](https://mlsanity.com/paper/2109.03300/) (2021)
- [SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models](https://mlsanity.com/paper/2210.07269/) (2022)
- [We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields](https://mlsanity.com/paper/2310.14870/) (2023)
- [Against Scale: Provocations and Resistances to Scale Thinking](https://mlsanity.com/paper/2010.08850/) (2020)
- [Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations](https://mlsanity.com/paper/2005.00962/) (2020)
- [Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation](https://mlsanity.com/paper/2402.01705/) (2024)
- [Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research](https://mlsanity.com/paper/2310.13915/) (2023)
- [The Contestation of Tech Ethics: A Sociotechnical Approach to Technology Ethics in Practice](https://mlsanity.com/paper/2106.01784/) (2021)
- [Classist Tools: Social Class Correlates with Performance in NLP](https://mlsanity.com/paper/2403.04445/) (2024)
- [Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning](https://mlsanity.com/paper/2306.01117/) (2023)

## Built on by (most cited first, 22 of 22)

- [The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models](https://mlsanity.com/paper/2507.16076/) (2025)
- [WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case](https://mlsanity.com/paper/2409.05653/) (2024)
- [One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization](https://mlsanity.com/paper/2601.18572/) (2026)
- [No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy](https://mlsanity.com/paper/2509.04404/) (2025)
- [Agree to Disagree? A Meta-Evaluation of LLM Misgendering](https://mlsanity.com/paper/2504.17075/) (2025)
- [Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMs](https://mlsanity.com/paper/2505.02456/) (2025)
- [On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions](https://mlsanity.com/paper/2504.06303/) (2025)
- [On the Mutual Influence of Gender and Occupation in LLM Representations](https://mlsanity.com/paper/2503.06792/) (2025)
- [Can Large Language Models Really Recognize Your Name?](https://mlsanity.com/paper/2505.14549/) (2025)
- [Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias](https://mlsanity.com/paper/2601.18486/) (2026)
- [Sports and Women's Sports: Gender Bias in Text Generation with Olympic Data](https://mlsanity.com/paper/2502.04218/) (2025)
- [Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models](https://mlsanity.com/paper/2407.06917/) (2024)
- [Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts](https://mlsanity.com/paper/2501.04316/) (2025)
- [Human-Centred LLM Privacy Audits: Findings and Frictions](https://mlsanity.com/paper/2603.12094/) (2026)
- [Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring](https://mlsanity.com/paper/2604.19984/) (2026)
- [Teaching and Critiquing Conceptualization and Operationalization in NLP](https://mlsanity.com/paper/2512.18505/) (2025)
- [ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues](https://mlsanity.com/paper/2604.01925/) (2026)
- [NLP needs Diversity outside of 'Diversity'](https://mlsanity.com/paper/2604.14595/) (2026)
- [Do Gender Cues Affect LLM Value Trade-offs? Evidence from a Controlled Decision Benchmark](https://mlsanity.com/paper/2606.02214/) (2026)
- [No PUN Intended: Plausible Unknown Names for Person-Centred LLM Evaluation](https://mlsanity.com/paper/2608.21206/) (2026)
- [WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ Identities](https://mlsanity.com/paper/2609.02651/) (2026)
- [Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring](https://mlsanity.com/paper/2609.22169/) (2026)

## 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.