Ethnographic Study Explores Reparative Approach to Data Work in AI Safety
Researchers conducted an ethnographic study of a civic-tech initiative that builds online safety datasets collaboratively with communities most affected by online harms, framing data work as a site for repair and redress. The initiative approaches AI safety from a feminist perspective, aiming to advance fair compensation for data workers and collective governance of AI datasets. The study argues that making AI truly responsible requires resetting accountability structures to center those most harmed by current data practices, rather than focusing solely on technical fixes like red teaming.
A paper accepted to ACM FAccT 2026 presents an ethnographic study of a civic-tech initiative that constructs datasets for training and benchmarking online safety AI systems using a feminist, community-centered methodology. Rather than relying on conventional data annotation pipelines, the initiative builds datasets collaboratively with individuals most impacted by online harms, positioning this process as a form of reparative justice. The researchers, drawing on Science and Technology Studies (STS) frameworks, trace the practical challenges the initiative faces in achieving just reward for data workers and establishing collective governance over the datasets produced. The paper critiques prevailing norms in AI development—including safety evaluations and red teaming—as insufficient if they do not address foundational questions about the relationships between human contributors and the systems they help create. Ultimately, the authors argue that genuine repair of data work requires recentering accountability around the people most excluded and harmed by current modes of dataset production, offering a vision for alternative futures in AI practice.
What's missing
The paper is an ethnographic study, so its findings may not generalize beyond the single civic-tech initiative examined. Key open questions include whether the reparative model studied is financially sustainable at scale, how the quality and coverage of collaboratively built safety datasets compare to conventionally produced ones, and whether the accountability structures proposed can be operationalized within mainstream AI development pipelines. The study does not appear to report quantitative outcomes measuring the effectiveness of the resulting safety datasets.
What different sources said
- arXiv cs.AICenter
Can Data Work be Reparative?
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