ArticleASSETS. Annual ACM Conference on Assistive Technologies2022
Data Representativeness in Accessibility Datasets: A Meta-Analysis.
Article in ASSETS. Annual ACM Conference on Assistive Technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Market shaping via digital public infrastructure for assistive technology in low- and middle-income countries.Frontiers in rehabilitation sciences · 2026Article
- Bridging Ontologies of Neurological Conditions: Towards Patient-centered Data Practices in Digital Phenotyping Research and Design.Proceedings of the ACM on human-computer interaction · 2025Article
- Levelling up as a fair solution in AI enabled cancer screening.Frontiers in digital health · 2025Article
- HCI and Older Adults: The Critical Turn and What Comes Next.Foundations and trends in human computer interaction · 2025Article
- Hevelius Report: Visualizing Web-Based Mobility Test Data For Clinical Decision and Learning Support.ASSETS. Annual ACM Conference on Assistive Technologies · 2024Article
- "We are at the mercy of others' opinion": Supporting Blind People in Recreational Window Shopping with AI-infused Technology.... International web for all conference. Web for All Conference · 2024Article
- Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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 (
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.