Reimagining Data Work: Participatory Annotation Workshops as Feminist Practice
2026-02-25 • Computers and Society
Computers and Society
AI summaryⓘ
The authors studied how AI depends on data workers, who often don't get enough recognition or respect for their skills. They focused on a project with journalists and activists working on gender violence stories, using workshops based on feminist ideas to encourage teamwork and share knowledge fairly. They found that balancing the details of context and agreement among people is tricky but important. They also discussed how to fairly acknowledge the work without making it feel like just a transaction. Overall, the authors suggest seeing AI work as a way to understand differences, care for each other, and build solidarity.
data annotationfeminist epistemologyparticipatory methodsgender-related violenceAI laborcontextual analysistactical consensusresearcher-participant dynamicssolidaritydata workers
Authors
Yujia Gao, Isadora Araujo Cruxên, Helena Suárez Val, Alessandra Jungs de Almeida, Catherine D'Ignazio, Harini Suresh
Abstract
AI systems depend on the invisible and undervalued labor of data workers, who are often treated as interchangeable units rather than collaborators with meaningful expertise. Critical scholars and practitioners have proposed alternative principles for data work, but few empirical studies examine how to enact them in practice. This paper bridges this gap through a case study of multilingual, iterative, and participatory data annotation processes with journalists and activists focused on news narratives of gender-related violence. We offer two methodological contributions. First, we demonstrate how workshops rooted in feminist epistemology can foster dialogue, build community, and disrupt knowledge hierarchies in data annotation. Second, drawing insights from practice, we deepen the analysis of existing feminist and participatory principles. We show that prioritizing context and pluralism in practice may require ``bounding'' context and working towards what we describe as a ``tactical consensus.'' We also explore tensions around materially acknowledging labor while resisting transactional researcher-participant dynamics. Through this work, we contribute to growing efforts to reimagine data and AI development as relational and political spaces for understanding difference, enacting care, and building solidarity across shared struggles.