Designing an Online Infrastructure for Collecting AI Data From People With Disabilities
Abstract
AI technology offers opportunities to expand virtual and physical access for people with disabilities. However, an important part of bringing these opportunities to fruition is ensuring that upcoming AI technology works well for people with a wide range of abilities. In this paper, we identify the lack of data from disabled populations as one of the challenges to training and benchmarking fair and inclusive AI systems. As a potential solution, we envision an online infrastructure that can enable large-scale, remote data contributions from disability communities. We investigate the motivations, concerns, and challenges that people with disabilities might experience when asked to collect and upload various forms of AI-relevant data through a semi-structured interview and an online survey that simulated a data contribution process by collecting example data files through an online portal. Based on our findings, we outline design guidelines for developers creating online infrastructures for gathering data from people with disabilities.
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Prior research suggested recruiting disabled people is a key challenge in creating disability-first datasets.
“Prior research suggested recruiting disabled people is a key challenge in creating disability-first datasets [78].”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [78]
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“We note that needing external assistance is not necessarily problematic, and indeed that interdependence [5] is important to and valued by many PWD; however, it is important for system designers to understand if and how assistance may be required for data upload tasks to create an accessible system.”
BackgroundGender and Dialect Bias in YouTube’s Automatic Captions
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“Meanwhile, a growing body of work has documented AI models’ degraded, and at times discriminatory, performance for users with disabilities 1 [30, 43, 52, 67, 92, 104, 115], and identified the lack of data representing disabled people as one of the root causes [12, 43, 64, 78, 88, 104].”
- Authors
- Park, J. S., Bragg, D., Kamar, E., & Morris, M. R.
- Affiliations
- Microsoft (United States) (US)
- Microsoft Research New England (United States) (US)
- Venue
- Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2021-03
- Pages
- 52-63
- OpenAlex
- W3128140567
- Cited by
- 50
- References
- 8
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 12 · 2022: 5 · 2021: 7
- FWCI
- 11.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Safety Research › Disability Rights and Representation
- Keywords
- people with disabilities, inclusive AI, algorithmic fairness, benchmarking, design guidelines, disability community
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