Activating the Annotator: Operationalising Lived Experience in Data Annotation
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Abstract (arXiv)
Perspectivist NLP has established that annotator positionality shapes labels for subjective tasks, but the field still lacks methods for operationalising it. Positionality is typically operationalised as a demographic form completed at recruitment: a static attribute recorded once and treated as a covariate, on the assumption that the knowledge it indexes is active when the annotator reads an item. This paper treats that assumption as a design problem and asks whether lived experience can instead be primed by the task itself. We introduce AXLE, a pre-annotation stage in which a scenario vignette anchors a short, axis-guided conversation that is synthesised into a first-person micro-narrative and returned to the annotator before labelling begins. In a 2x2 between-subjects study (104 annotators, 30 items, 1,040 annotations) on ambiguous hate speech spanning immigration, religion and gender, elicitation was associated with higher identity resonance and higher sensitivity to harm. It also increased label entropy and lowered inter-annotator agreement: the stage appears to surface plurality rather than manufacture consensus, which distinguishes it from annotator training, whose success criterion is convergence.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 44 references that are in the map. First posted on arXiv 8 Oct 2026.
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15 of 44 references.
References · 15
- 2021Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotationscited by 314
- 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
- 2024Quantifying the Persona Effect in LLM Simulationscited by 169
- 2021MultiVerS: Improving scientific claim verification with weak supervision and full-document contextcited by 143
- 2021Toward a Perspectivist Turn in Ground Truthing for Predictive Computingcited by 130
- 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 126
- 2023Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Promptingcited by 81
- 2022Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotationcited by 69
- 2024The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labelscited by 38
- 2025Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptionscited by 33
- 2023Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Taskscited by 29
- 2024"Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled Peoplecited by 10
3 more
- 2026"Label from Somewhere": Reflexive Annotating for Situated AI Alignmentcited by 4
- 2026Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stuttercited by 2
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 2
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