Data labeling / ethics and bias mitigationdataset creation and annotation ethicsParticipation documented
Rehumanized Crowdsourcing
Syracuse, United States
The framework explicitly re-centers human labelers in ML annotation workflows to address bias and ethics.
human-centered designlabor transparencyethics in annotationdata collectionlabelingquality control
- Region
- North America
- Lead organization
- Syracuse University with Figure Eight
- Organization type
- university + industry partnership
- Technology group
- data labeling workflows for machine learning
- Activity status
- published_case
- Start year
- 2019
- Last updated
- Mar 25, 2026
- Participation mode
- crowdworker-centered labeling framework design
- Participants
- crowdworkers; researchers; labeling-platform stakeholders
- Methods
- crowdsourcing; framework design; ethics-focused workflow redesign
- AI lifecycle stages
- data collection; labeling; quality control
- Evidence summary
- The framework explicitly re-centers human labelers in ML annotation workflows to address bias and ethics.
- Atlas assessment
- Cautious · Medium confidence · evidence grade B
- Uncertainty
- Worker-centered design is substantive, but the project is closer to participatory workflow redesign than shared governance.
- Participation group
- Community In The Loop
- Technology description
- data labeling workflows for machine learning
- Funding
- Not documented
- Region of activity
- national
- Verification status
- Paper Verified
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