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

TL;DR

This paper introduces reflexive annotating, a method encouraging crowd workers to reflect on their social position, revealing how annotator context influences language model alignment judgments.

Contribution

It presents a novel reflexive annotation approach that captures intersectional reasoning and positional humility, enriching understanding of annotator influence.

Findings

Reflexive annotating elicits intersectional reasoning and positional humility.

It surfaces epistemic metadata beyond static demographics.

The method reveals tensions between reflexive engagement and emotional exposure.

Abstract

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.