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

arXiv: https://arxiv.org/abs/2308.13089 · HTML: https://mlsanity.com/paper/2308.13089/

## 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 (most cited first, 6 of 6)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [On Measures of Biases and Harms in NLP](https://mlsanity.com/paper/2108.03362/) (2021)
- [Nationality Bias in Text Generation](https://mlsanity.com/paper/2302.02463/) (2023)
- [Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles](https://mlsanity.com/paper/2308.04346/) (2023)
- [Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models](https://mlsanity.com/paper/2111.13259/) (2021)

## Built on by (most cited first, 9 of 9)

- [Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval](https://mlsanity.com/paper/2407.20371/) (2024)
- [An Audit on the Perspectives and Challenges of Hallucinations in NLP](https://mlsanity.com/paper/2404.07461/) (2024)
- [Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses](https://mlsanity.com/paper/2410.22349/) (2024)
- [From Melting Pots to Misrepresentations: Exploring Harms in Generative AI](https://mlsanity.com/paper/2403.10776/) (2024)
- [DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence](https://mlsanity.com/paper/2509.04499/) (2025)
- [Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI](https://mlsanity.com/paper/2410.15467/) (2024)
- [Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis](https://mlsanity.com/paper/2310.00347/) (2023)
- [Race and Privacy in Broadcast Police Communications](https://mlsanity.com/paper/2407.01817/) (2024)
- [From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse](https://mlsanity.com/paper/2509.14712/) (2025)

## 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.