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

2026-01-25

AI Summary

This work proposes a “reflexive annotation” approach to AI alignment, challenging the conventional treatment of annotators as interchangeable instruments by foregrounding how their social positions shape subjective judgments. Through semi-structured interviews and contextualized metadata collection, the study integrates human-computer interaction and value alignment theories in a qualitative user investigation. The method successfully captures cognitive metadata that transcends static demographic categories, revealing dynamic dimensions such as intersectional reasoning, epistemic humility, and shifts in perspective. These findings illuminate the tensions between cognition and affect inherent in the annotation process and offer a novel pathway toward more context-sensitive and positionally aware AI alignment practices.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• AI alignment
• annotator positionality
• reflexive annotating
• situated judgment
• value elicitation

Innovation

• reflexive annotating
• positionality
• AI alignment
• epistemic metadata
• situated judgment