# Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens

arXiv: https://arxiv.org/abs/2110.00521 · HTML: https://mlsanity.com/paper/2110.00521/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification](https://mlsanity.com/paper/1903.04561/) (2019)
- [On Measuring Social Biases in Sentence Encoders](https://mlsanity.com/paper/1903.10561/) (2019)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Measuring Bias in Contextualized Word Representations](https://mlsanity.com/paper/1906.07337/) (2019)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems](https://mlsanity.com/paper/1805.04508/) (2018)
- [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018)
- [Social Biases in NLP Models as Barriers for Persons with Disabilities](https://mlsanity.com/paper/2005.00813/) (2020)
- [Learning Gender-Neutral Word Embeddings](https://mlsanity.com/paper/1809.01496/) (2018)
- [Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases](https://mlsanity.com/paper/2006.03955/) (2020)
- [Reducing Gender Bias in Abusive Language Detection](https://mlsanity.com/paper/1808.07231/) (2018)
- [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://mlsanity.com/paper/1904.04047/) (2019)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Intersectional Bias in Hate Speech and Abusive Language Datasets](https://mlsanity.com/paper/2005.05921/) (2020)
- [AI Fairness for People with Disabilities: Point of View](https://mlsanity.com/paper/1811.10670/) (2018)
- [Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis](https://mlsanity.com/paper/2011.12086/) (2020)

## Built on by (most cited first, 30 of 30)

- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges](https://mlsanity.com/paper/2305.06969/) (2023)
- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [Identifying and Improving Disability Bias in GPT-Based Resume Screening](https://mlsanity.com/paper/2402.01732/) (2024)
- [NBIAS: A Natural Language Processing Framework for Bias Identification in Text](https://mlsanity.com/paper/2308.01681/) (2023)
- [Global Voices, Local Biases: Socio-Cultural Prejudices across Languages](https://mlsanity.com/paper/2310.17586/) (2023)
- [Weakly Supervised Detection of Hallucinations in LLM Activations](https://mlsanity.com/paper/2312.02798/) (2023)
- [Fairness in Language Models Beyond English: Gaps and Challenges](https://mlsanity.com/paper/2302.12578/) (2023)
- [Queer People are People First: Deconstructing Sexual Identity Stereotypes in Large Language Models](https://mlsanity.com/paper/2307.00101/) (2023)
- [Fairness Definitions in Language Models Explained](https://mlsanity.com/paper/2407.18454/) (2024)
- [MIST: Mitigating Intersectional Bias with Disentangled Cross-Attention Editing in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2403.19738/) (2024)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [This Prompt is Measuring <MASK>: Evaluating Bias Evaluation in Language Models](https://mlsanity.com/paper/2305.12757/) (2023)
- [Data Representativeness in Accessibility Datasets: A Meta-Analysis](https://mlsanity.com/paper/2207.08037/) (2022)
- [Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMs](https://mlsanity.com/paper/2508.15831/) (2025)
- [ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios](https://mlsanity.com/paper/2510.10998/) (2025)
- [A Disability Lens towards Biases in GPT-3 Generated Open-Ended Languages](https://mlsanity.com/paper/2206.11993/) (2022)
- [Fairness Certification for Natural Language Processing and Large Language Models](https://mlsanity.com/paper/2401.01262/) (2024)
- [A Note on Bias to Complete](https://mlsanity.com/paper/2402.11710/) (2024)
- [Theories of "Sexuality" in Natural Language Processing Bias Research](https://mlsanity.com/paper/2506.22481/) (2025)
- [Cross-Lingual Stability and Bias in Instruction-Tuned Language Models for Humanitarian NLP](https://mlsanity.com/paper/2510.22823/) (2025)
- [QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Tasks](https://mlsanity.com/paper/2601.20731/) (2026)
- [Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users](https://mlsanity.com/paper/2206.12368/) (2022)
- [Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Models](https://mlsanity.com/paper/2509.00963/) (2025)
- [A Multifaceted Analysis of Social Biases in Large Language Models](https://mlsanity.com/paper/2512.15792/) (2025)
- [Auditing Disability Representation in Vision-Language Models](https://mlsanity.com/paper/2601.17348/) (2026)
- [Investigating Disability Representations in Text-to-Image Models](https://mlsanity.com/paper/2602.04687/) (2026)
- [Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Once](https://mlsanity.com/paper/2604.01504/) (2026)
- [Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs](https://mlsanity.com/paper/2605.20191/) (2026)
- [Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCH](https://mlsanity.com/paper/2607.08515/) (2026)

## Abstract (arXiv)

Much of the world's population experiences some form of disability during their lifetime. Caution must be exercised while designing natural language processing (NLP) systems to prevent systems from inadvertently perpetuating ableist bias against people with disabilities, i.e., prejudice that favors those with typical abilities. We report on various analyses based on word predictions of a large-scale BERT language model. Statistically significant results demonstrate that people with disabilities can be disadvantaged. Findings also explore overlapping forms of discrimination related to interconnected gender and race identities.