mlsanityData to 14 Oct 2026

Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition

arXiv 1910.10872 ↗First posted 24 Oct 2019cs.IR cs.CLcited by 28

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Abstract (arXiv)

We study the bias in several state-of-the-art named entity recognition (NER) models---specifically, a difference in the ability to recognize male and female names as PERSON entity types. We evaluate NER models on a dataset containing 139 years of U.S. census baby names and find that relatively more female names, as opposed to male names, are not recognized as PERSON entities. We study the extent of this bias in several NER systems that are used prominently in industry and academia. In addition, we also report a bias in the datasets on which these models were trained. The result of this analysis yields a new benchmark for gender bias evaluation in named entity recognition systems. The data and code for the application of this benchmark will be publicly available for researchers to use.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2019.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 8; 2024: 1; 2025: 1; 2026: 1.

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  1. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  2. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  3. 2021On Measures of Biases and Harms in NLPcited by 81
  4. 2023Large Language Models and Knowledge Graphs: Opportunities and Challengescited by 71
  5. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  6. 2021Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resourcescited by 30
  7. 2022NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challengecited by 20
  8. 2023Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologiescited by 16
  9. 2021Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Modelscited by 15
  10. 2021The Arabic Parallel Gender Corpus 2.0: Extensions and Analysescited by 12
  11. 2021Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Biascited by 11
  12. 2023In the Name of Fairness: Assessing the Bias in Clinical Record De-identificationcited by 9
16 more
  1. 2022Towards Multi-Objective Statistically Fair Federated Learningcited by 8
  2. 2023Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoningcited by 8
  3. 2023Mitigating Bias for Question Answering Models by Tracking Bias Influencecited by 8
  4. 2022User-Centric Gender Rewritingcited by 7
  5. 2022ER-Test: Evaluating Explanation Regularization Methods for Language Modelscited by 6
  6. 2022Measuring Geographic Performance Disparities of Offensive Language Classifierscited by 5
  7. 2022The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphscited by 4
  8. 2022A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Modelscited by 4
  9. 2025Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?cited by 4
  10. 2023Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewritingcited by 3
  11. 2024BEADs: Bias Evaluation Across Domainscited by 3
  12. 2021NewsEdits: A Dataset of Revision Histories for News Articles (Technical Report: Data Processing)cited by 2
  13. 2023National Origin Discrimination in Deep-learning-powered Automated Resume Screeningcited by 2
  14. 2023Towards Fair and Explainable AI using a Human-Centered AI Approachcited by 1
  15. 2026Large Language Models for Geolocation Extraction in Humanitarian Crisis Responsecited by 1
  16. 2022An Approach to Ensure Fairness in News Articlescited by 0