推进数据公平：NLP数据实践中的从业者责任与问责
Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices
AI总结：
本研究以美国NLP数据从业者视角为中心，结合多层级AI治理框架，揭示商业目标与公平承诺之间的张力，并提出需要结构性治理改革来支持从业者能动性和社区同意，从而推进NLP数据公平。
摘要：
虽然已有研究聚焦于揭示和审计算法偏见以确保公平的AI发展，但关于NLP从业者——那些直接参与数据集开发、标注和部署的人员——如何感知和应对NLP数据公平问题，人们知之甚少。本研究是首批以从业者视角为中心的研究之一，将他们的经验与多层级AI治理框架联系起来，并提出连接技术、政策和社区领域的参与式建议。基于2024年的一项问卷调查和焦点小组，我们考察了美国NLP数据从业者如何概念化公平性、应对组织和系统性约束，以及参与新兴的治理举措，如美国《AI权利法案》。研究结果揭示了商业目标与公平承诺之间持续存在的张力，以及对更具参与性和问责性的数据工作流程的呼吁。我们批判性地参与了关于数据多样性和“多样性洗白”的辩论，认为改善NLP公平性需要结构性的治理改革，以支持从业者的能动性和社区同意。
英文摘要：
While research has focused on surfacing and auditing algorithmic bias to ensure equitable AI development, less is known about how NLP practitioners - those directly involved in dataset development, annotation, and deployment - perceive and navigate issues of NLP data equity. This study is among the first to center practitioners' perspectives, linking their experiences to a multi-scalar AI governance framework and advancing participatory recommendations that bridge technical, policy, and community domains. Drawing on a 2024 questionnaire and focus group, we examine how U.S.-based NLP data practitioners conceptualize fairness, contend with organizational and systemic constraints, and engage emerging governance efforts such as the U.S. AI Bill of Rights. Findings reveal persistent tensions between commercial objectives and equity commitments, alongside calls for more participatory and accountable data workflows. We critically engage debates on data diversity and diversity washing, arguing that improving NLP equity requires structural governance reforms that support practitioner agency and community consent.
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。
