The Ecological Fallacy in Annotation: Modeling Human Label Variation goes beyond Sociodemographics
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Many NLP tasks exhibit human label variation, where different annotators give different labels to the same texts.This variation is known to depend, at least in part, on the sociodemographics of annotators.Recent research aims to model individual annotator behaviour rather than predicting aggregated labels, and we would expect that sociodemographic information is useful for these models.On the other hand, the ecological fallacy states that aggregate group behaviour, such as the behaviour of the average female annotator, does not necessarily explain individual behaviour.To account for sociodemographics in models of individual annotator behaviour, we introduce group-specific layers to multi-annotator models.In a series of experiments for toxic content detection, we find that explicitly accounting for sociodemographic attributes in this way does not significantly improve model performance.This result shows that individual annotation behaviour depends on much more than just sociodemographics.
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Past work in NLP has found a mixed trend: for some datasets, demographics explain little variation, while for others, demographics explain substantial variation.
“indeed, past work in NLP has found a mixed trend, where for some datasets, demographics explain little variation (Orlikowski et al., 2023), while for others, demographics explain substantial variation (Larimore et al., 2021;Sap et al., 2022;Pei and Jurgens, 2023;Wan et al., 2023).”
From NUTMEG: Separating Signal From Noise in Annotator Disagreement (2025) · cites this paper as Orlikowski et al., 2023
The Results (§5) find no statistically significant predictive gain from adding sociodemographic information in the dataset studied, supporting the cited clause’s general direction. But the paper does not measure how much label variation demographics explain; its finding is narrower and specific to adding group-specific layers to multi-annotator models. (LLM, conf=0.82)
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NUTMEG: Separating Signal From Noise in Annotator Disagreement
Ivey, J., Gauch, S., & Jurgens, D. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2025.emnlp-main.144
- Authors
- Orlikowski, M., Röttger, P., Cimiano, P., & Hovy, D.
- Affiliations
- Bielefeld University (DE)
- University of Oxford (GB)
- Bocconi University (IT)
- Venue
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2023
- Pages
- 1017-1029
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4385571603
- Cited by
- 9
- References
- 2
- Citations by year
- 2026: 2 · 2025: 6 · 2023: 1
- FWCI
- 1.43
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- European Commission (HORIZON2020)
- Volkswagen Foundation
- H2020 European Research Council (949944)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- ecological fallacy, annotation, sociodemographic attributes, toxic content detection, annotation bias
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