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
- Participation group
- Community In The Loop
- Technology description
- data labeling workflows for machine learning
- Funding
- Not documented
- Region of activity
- national
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