mlsanityData to 14 Oct 2026

AI Fairness for People with Disabilities: Point of View

arXiv 1811.10670 ↗First posted 26 Nov 2018cs.AI cs.CYcited by 16

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Abstract (arXiv)

We consider how fair treatment in society for people with disabilities might be impacted by the rise in the use of artificial intelligence, and especially machine learning methods. We argue that fairness for people with disabilities is different to fairness for other protected attributes such as age, gender or race. One major difference is the extreme diversity of ways disabilities manifest, and people adapt. Secondly, disability information is highly sensitive and not always shared, precisely because of the potential for discrimination. Given these differences, we explore definitions of fairness and how well they work in the disability space. Finally, we suggest ways of approaching fairness for people with disabilities in AI applications.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Nov 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 5; 2025: 3; 2026: 2.

16 papers.

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  1. 2021Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lenscited by 30
  2. 2021Intersectional Bias in Causal Language Modelscited by 24
  3. 2023Large Language Model (LLM) Bias Index -- LLMBIcited by 14
  4. 2021Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Modelscited by 13
  5. 2024Lazy Data Practices Harm Fairness Researchcited by 10
  6. 2024"Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled Peoplecited by 10
  7. 2022Data Representativeness in Accessibility Datasets: A Meta-Analysiscited by 6
  8. 2022Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustnesscited by 5
  9. 2024WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Userscited by 5
  10. 2025Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learningcited by 5
  11. 2025ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarioscited by 5
  12. 2025Bridging Text and Video Generation: A Surveycited by 2
4 more
  1. 2024A Transformer-Based Multi-Stream Approach for Isolated Iranian Sign Language Recognitioncited by 1
  2. 2024Localized Gaussians as Self-Attention Weights for Point Clouds Correspondencecited by 0
  3. 2026Investigating Disability Representations in Text-to-Image Modelscited by 0
  4. 2026Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCHcited by 0