AI alignment relies on annotator judgments, yet annotation pipelines often treat annotators as interchangeable, obscuring how their social position shapes annotation. We introduce reflexive annotating as a probe that invites crowd workers to reflect on how their positionality informs subjective annotation judgments in a language model alignment context. Through a qualitative study with crowd workers (N=30) and follow-up interviews (N=5), we examine how our probe shapes annotators' behaviour, experience, and the situated metadata it elicits. We find that reflexive annotating captures epistemic metadata beyond static demographics by eliciting intersectional reasoning, surfacing positional humility, and nudging viewpoint change. Crucially, we also denote tensions between reflexive engagement and affective demands such as emotional exposure. We discuss the implications of our work for richer value elicitation and alignment practices that treat annotator judgments as situated and selectively integrate positional metadata.

翻译：人工智能对齐依赖于标注者的判断，然而标注流程通常将标注者视为可互换的个体，这掩盖了其社会位置如何影响标注过程。我们提出反思性标注作为一种探针方法，邀请众包工作者在语言模型对齐的语境中反思其立场如何影响主观标注判断。通过对众包工作者（N=30）的质性研究和后续访谈（N=5），我们考察了该探针如何影响标注者的行为、体验及其引出的情境化元数据。研究发现，反思性标注通过引发交叉性推理、展现立场谦逊性以及推动观点转变，能够捕捉超越静态人口统计特征的认知元数据。值得注意的是，我们也揭示了反思性参与与情感需求（如情绪暴露）之间的张力。本文讨论了本研究对价值获取实践的意义：通过将标注判断视为情境化产物，并选择性整合立场元数据，可为对齐实践提供更丰富的价值基础。