Participatory AI Atlas
Accessibility / dataset stewardshipaccessibility and responsible data stewardshipParticipation documented

Contributing to Accessibility Datasets

College Park, United States

Blind participants directly contributed data and reflected on conditions for sharing study data in accessibility AI datasets.

inclusive designdisability-first designresponsible data stewardshipdata collectiondata governanceproblem formulation
Project documentationEvidence source
Overview
Region
North America
Lead organization
University of Maryland / Includet project
Organization type
university
Technology group
accessibility datasets for AI systems
Activity status
published_case
Start year
2023
Last updated
Mar 25, 2026
Participation documentation
Participation mode
blind-participant data capture and reflection on dataset sharing
Participants
blind participants
Methods
data capture; reflective interviews; dataset-sharing deliberation
AI lifecycle stages
data collection; data governance; problem formulation
Evidence summary
Blind participants directly contributed data and reflected on conditions for sharing study data in accessibility AI datasets.
Atlas assessment
Core · High confidence · evidence grade A
Uncertainty
Public evidence indicates that stakeholders directly shaped AI design, data, deployment, evaluation, or oversight; residual uncertainty concerns the exact degree of shared decision power rather than project existence.
Project details
Participation group
Co Design
Technology description
accessibility datasets for AI systems
Funding
Not documented
Region of activity
local
Verification status
Live Verified
Links
Project documentationOfficial pageEvidence sourceResearch paperPreprint

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