mlsanityData to 14 Oct 2026

Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lens

arXiv 2308.13089 ↗First posted 24 Aug 2023cs.CLcited by 9

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Abstract (arXiv)

The rapid growth in the usage and applications of Natural Language Processing (NLP) in various sociotechnical solutions has highlighted the need for a comprehensive understanding of bias and its impact on society. While research on bias in NLP has expanded, several challenges persist that require attention. These include the limited focus on sociodemographic biases beyond race and gender, the narrow scope of analysis predominantly centered on models, and the technocentric implementation approaches. This paper addresses these challenges and advocates for a more interdisciplinary approach to understanding bias in NLP. The work is structured into three facets, each exploring a specific aspect of bias in NLP.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 24 Aug 2023.

Builds on

6 of 11 references.

References · 6

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  2. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  3. 2021On Measures of Biases and Harms in NLPcited by 81
  4. 2023Nationality Bias in Text Generationcited by 50
  5. 2023Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articlescited by 16
  6. 2021Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Modelscited by 13

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Cited by year

Citing papers per year: 2023: 1; 2024: 6; 2025: 2.

9 papers.

  1. 2024Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrievalcited by 51
  2. 2024An Audit on the Perspectives and Challenges of Hallucinations in NLPcited by 17
  3. 2024Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responsescited by 15
  4. 2024From Melting Pots to Misrepresentations: Exploring Harms in Generative AIcited by 13
  5. 2025DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidencecited by 10
  6. 2024Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AIcited by 6
  7. 2023Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysiscited by 4
  8. 2024Race and Privacy in Broadcast Police Communicationscited by 1
  9. 2025From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discoursecited by 0