# Data Representativeness in Accessibility Datasets: A Meta-Analysis

arXiv: https://arxiv.org/abs/2207.08037 · HTML: https://mlsanity.com/paper/2207.08037/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 200 references that are in the map.
- First posted on arXiv 16 Jul 2022.

## Builds on (most cited first, 28 of 28)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Social Biases in NLP Models as Barriers for Persons with Disabilities](https://mlsanity.com/paper/2005.00813/) (2020)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age](https://mlsanity.com/paper/1908.04913/) (2019)
- [Diversity in Faces](https://mlsanity.com/paper/1901.10436/) (2019)
- [Unpacking the Interdependent Systems of Discrimination: Ableist Bias in NLP Systems through an Intersectional Lens](https://mlsanity.com/paper/2110.00521/) (2021)
- [Fingerspelling recognition in the wild with iterative visual attention](https://mlsanity.com/paper/1908.10546/) (2019)
- [Diversity and Inclusion Metrics in Subset Selection](https://mlsanity.com/paper/2002.03256/) (2020)
- [Evaluating Gender Bias in Natural Language Inference](https://mlsanity.com/paper/2105.05541/) (2021)
- [Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination](https://mlsanity.com/paper/1912.06171/) (2019)
- [Toward Fairness in AI for People with Disabilities: A Research Roadmap](https://mlsanity.com/paper/1907.02227/) (2019)
- [American Sign Language fingerspelling recognition in the wild](https://mlsanity.com/paper/1810.11438/) (2018)
- [How to be Fair and Diverse?](https://mlsanity.com/paper/1610.07183/) (2016)
- [AI Fairness for People with Disabilities: Point of View](https://mlsanity.com/paper/1811.10670/) (2018)
- [Understanding the Representation and Representativeness of Age in AI Data Sets](https://mlsanity.com/paper/2103.09058/) (2021)
- [What Do We Mean by "Accessibility Research"? A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019](https://mlsanity.com/paper/2101.04271/) (2021)
- [Diversity in Sociotechnical Machine Learning Systems](https://mlsanity.com/paper/2107.09163/) (2021)
- [What is the Point of Fairness? Disability, AI and The Complexity of Justice](https://mlsanity.com/paper/1908.01024/) (2019)
- [Sharing Practices for Datasets Related to Accessibility and Aging](https://mlsanity.com/paper/2108.10665/) (2021)
- [Fairness Issues in AI Systems that Augment Sensory Abilities](https://mlsanity.com/paper/1908.07333/) (2019)

## Built on by (most cited first, 6 of 6)

- [Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMs](https://mlsanity.com/paper/2508.15831/) (2025)
- ["We are at the mercy of others' opinion": Supporting Blind People in Recreational Window Shopping with AI-infused Technology](https://mlsanity.com/paper/2405.06611/) (2024)
- [How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People](https://mlsanity.com/paper/2602.13469/) (2026)
- [On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms](https://mlsanity.com/paper/2310.15848/) (2023)
- [Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices](https://mlsanity.com/paper/2508.10071/) (2025)
- [The Gold in Bias: Maturing the AI Design Process through Verification](https://mlsanity.com/paper/2609.29730/) (2026)

## Abstract (arXiv)

As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this paper, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets - datasets sourced from people with disabilities and older adults - that can potentially play an important role in mitigating bias for inclusive AI-infused applications. We examine the current state of representation within datasets sourced by people with disabilities by reviewing publicly-available information of 190 datasets, we call these accessibility datasets. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. Additionally, we investigate how the sensitive and complex nature of demographic variables makes classification difficult and inconsistent (e.g., gender, race & ethnicity), with the source of labeling often unknown. By reflecting on the current challenges and opportunities for representation of disabled data contributors, we hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.