Can Data Work be Reparative?
2026-06-08 • Computers and Society
Computers and SocietyArtificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors studied a community-driven project that creates datasets to improve online safety from a feminist viewpoint. They focus on involving people who are most affected by online harms in building these datasets. The paper discusses the difficulties in fairly rewarding contributors and managing the data together. The authors suggest that fixing problems in data work and AI requires changing how people are held accountable, putting the harmed individuals at the center rather than just the technology. This approach challenges common practices and envisions a more responsible way to produce data and AI systems.
Ethnographic studyCivic-techOnline safetyFeminist perspectiveDataset productionReparative justiceCollective governanceAccountabilityAI safety evaluationData work
Authors
Srravya Chandhiramowuli, Ding Wang, Alex Taylor
Abstract
We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and benchmarking online safety systems. They aim to respond to online safety concerns from a feminist perspective, by building safety datasets collaboratively with those most impacted by online harms. In this paper, we examine how this approach aims to reorient data work as a site for repair and redress, and trace the struggles they encounter in the process. Specifically, we draw attention to the challenges and tensions involved in advancing just reward for data work and collective governance of AI datasets. Examining these challenges through an STS-informed lens of reparative justice and repair, we argue that the work of repairing data work (and AI) lies, fundamentally, in resetting the ties of accountability. At a time heightened emphasis on efforts like safety evaluations and red teaming to make AI more responsible, we highlight the need to confront foundational questions about how the humans involved in these efforts relate to the datasets and systems they help produce. A reparative lens demands that we interrupt prevailing norms of data work and place at their centre, not AI or datasets, but those most harmed by the neglect, oversight and exclusion animated in the current modes of dataset production. This, we argue, offers a bold vision for responsibility and contributes towards a critical agenda for building alternative futures of data and AI practice.