重新想象数据工作：参与式标注工作坊作为女权主义实践
Reimagining Data Work: Participatory Annotation Workshops as Feminist Practice
AI总结：
本文通过参与式标注工作坊探讨如何在数据工作中实现女权主义实践，强调情境与多元主义的重要性，提出战术共识的概念，并探讨劳动承认与交易动态的矛盾。
摘要：
人工智能系统依赖于那些被忽视和低估的数据工作者的劳动，这些劳动者往往被当作可替代的单位，而不是具有有意义专业知识的合作者。批判学者和实践者提出了替代的数据工作原则，但很少有实证研究探讨如何在实践中实施这些原则。本文通过一个多语言、迭代和参与式的数据标注过程案例研究，与专注于性别相关暴力新闻叙述的记者和活动人士合作。我们提出了两种方法论贡献。首先，我们展示了扎根于女权主义知识论的工作坊如何促进对话、建立社区并打破知识等级制度。其次，通过实践中的洞察，我们深化了现有女权主义和参与式原则的分析。我们表明，在实践中优先考虑情境和多元主义可能需要“界定”情境，并朝着我们描述为“战术共识”的方向努力。我们还探讨了在物质上承认劳动的同时，如何抵制交易性的研究者-参与者动态。通过这项工作，我们为重新想象数据和人工智能开发作为理解差异、实施关怀和在共同斗争中建立团结的空间做出了贡献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
