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.

翻译：人工智能系统依赖于数据工作者无形且被低估的劳动，这些工作者常被视为可互换的单元，而非具备实质专业知识的合作者。批判学者与实践者已提出数据工作的替代性原则，但鲜有实证研究探讨如何在实践中落实这些原则。本文通过一项聚焦性别暴力新闻叙事、与记者和活动家合作的多语言、迭代式参与式数据标注流程的案例研究，弥合了这一差距。我们提出两项方法论贡献：首先，我们展示了植根于女性主义认识论的工作坊如何能在数据标注中促进对话、构建社群并打破知识等级结构；其次，基于实践洞察，我们深化了对现有女性主义及参与式原则的分析。我们证明，在实践中优先考虑语境与多元性可能需要“界定”语境边界，并努力达成我们称之为“策略性共识”的目标。同时，我们探讨了在物质层面承认劳动价值与抵制交易式研究者-参与者动态之间的张力。通过此项工作，我们为日益增长的、将数据与人工智能开发重新构想为理解差异、践行关怀并在共同斗争中构建团结的关系性政治空间的努力作出贡献。