"label from Somewhere"

Reflexive Annotating for Situated AI Alignment

Conference Paper (2026)
Author(s)

Anne Arzberger (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Céline Offerman (TU Delft - Industrial Design Engineering)

Ujwal Gadiraju (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Alessandro Bozzon (TU Delft - Industrial Design Engineering)

Jie Yang (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Web Information Systems
DOI related publication
https://doi.org/10.1145/3805689.3812268 Final published version
More Info
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Publication Year
2026
Language
English
Research Group
Web Information Systems
Pages (from-to)
1656-1680
Publisher
ACM
ISBN (electronic)
9798400725968
Event
9th Annual ACM Conference on Fairness, Accountability, and Transparency, ACM FAccT 2026 (2026-06-25 - 2026-06-28), Montreal, Canada
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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), including 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.