# "Label from Somewhere": Reflexive Annotating for Situated AI Alignment

arXiv: https://arxiv.org/abs/2601.17937 · HTML: https://mlsanity.com/paper/2601.17937/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2026.

## Built on by (most cited first, 4 of 4)

- [Position: Align AI to Our Aspirations, Not Our Flaws](https://mlsanity.com/paper/2606.13755/) (2026)
- [Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining](https://mlsanity.com/paper/2607.23175/) (2026)
- [Activating the Annotator: Operationalising Lived Experience in Data Annotation](https://mlsanity.com/paper/2610.12701/) (2026)
- [Model vs. User: Whose Values Prevail? Inference-Time Value Alignment in Multi-Turn Conversations](https://mlsanity.com/paper/2610.19817/) (2026)

## Abstract (arXiv)

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.