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Assessing Demographic Bias in Named Entity Recognition

arXiv 2008.03415 ↗First posted 8 Aug 2020cs.CL cs.CY cs.IRcited by 23

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Abstract (arXiv)

Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text. In this work, we assess the bias in various Named Entity Recognition (NER) systems for English across different demographic groups with synthetically generated corpora. Our analysis reveals that models perform better at identifying names from specific demographic groups across two datasets. We also identify that debiased embeddings do not help in resolving this issue. Finally, we observe that character-based contextualized word representation models such as ELMo results in the least bias across demographics. Our work can shed light on potential biases in automated KB generation due to systematic exclusion of named entities belonging to certain demographics.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Aug 2020.

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Citing papers per year: 2021: 2; 2022: 9; 2023: 6; 2024: 4; 2025: 1; 2026: 1.

23 papers.

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  1. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 72
  2. 2022Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversalcited by 40
  3. 2024Extrinsic Evaluation of Cultural Competence in Large Language Modelscited by 24
  4. 2021Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agencycited by 22
  5. 2024Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictionscited by 17
  6. 2023In the Name of Fairness: Assessing the Bias in Clinical Record De-identificationcited by 9
  7. 2022Behind the Mask: Demographic bias in name detection for PII maskingcited by 7
  8. 2023Examining risks of racial biases in NLP tools for child protective servicescited by 6
  9. 2022The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphscited by 4
  10. 2022TweetNERD -- End to End Entity Linking Benchmark for Tweetscited by 4
  11. 2022A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Modelscited by 4
  12. 2023Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewritingcited by 3
11 more
  1. 2023Automated Testing and Improvement of Named Entity Recognition Systemscited by 3
  2. 2024BEADs: Bias Evaluation Across Domainscited by 3
  3. 2022NERDA-Con: Extending NER models for Continual Learning -- Integrating Distinct Tasks and Updating Distribution Shiftscited by 2
  4. 2023Alt-Text with Context: Improving Accessibility for Images on Twittercited by 2
  5. 2022CTM -- A Model for Large-Scale Multi-View Tweet Topic Classificationcited by 1
  6. 2022Robust Candidate Generation for Entity Linking on Short Social Media Textscited by 1
  7. 2023Towards Fair and Explainable AI using a Human-Centered AI Approachcited by 1
  8. 2025Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generationcited by 1
  9. 2026Large Language Models for Geolocation Extraction in Humanitarian Crisis Responsecited by 1
  10. 2022#maskUp: Selective Attribute Encryption for Sensitive Vocalization for English language on Social Media Platformscited by 0
  11. 2024Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotationcited by 0