# The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographics

arXiv: https://arxiv.org/abs/2306.11559 · HTML: https://mlsanity.com/paper/2306.11559/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation](https://mlsanity.com/paper/2205.00501/) (2022)
- [Designing Toxic Content Classification for a Diversity of Perspectives](https://mlsanity.com/paper/2106.04511/) (2021)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://mlsanity.com/paper/2109.09483/) (2021)
- [Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detection](https://mlsanity.com/paper/2106.15896/) (2021)
- [Like trainer, like bot? Inheritance of bias in algorithmic content moderation](https://mlsanity.com/paper/1707.01477/) (2017)
- [Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets](https://mlsanity.com/paper/1709.09500/) (2017)
- [Towards Equal Gender Representation in the Annotations of Toxic Language Detection](https://mlsanity.com/paper/2106.02183/) (2021)

## Built on by (most cited first, 30 of 46)

- [Quantifying the Persona Effect in LLM Simulations](https://mlsanity.com/paper/2402.10811/) (2024)
- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
- [The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels](https://mlsanity.com/paper/2405.05860/) (2024)
- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks](https://mlsanity.com/paper/2311.09743/) (2023)
- [Value Profiles for Encoding Human Variation](https://mlsanity.com/paper/2503.15484/) (2025)
- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Hate Personified: Investigating the role of LLMs in content moderation](https://mlsanity.com/paper/2410.02657/) (2024)
- [Annotation alignment: Comparing LLM and human annotations of conversational safety](https://mlsanity.com/paper/2406.06369/) (2024)
- [Personas with Attitudes: Controlling LLMs for Diverse Data Annotation](https://mlsanity.com/paper/2410.11745/) (2024)
- [Annotator-Centric Active Learning for Subjective NLP Tasks](https://mlsanity.com/paper/2404.15720/) (2024)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives](https://mlsanity.com/paper/2311.05074/) (2023)
- [Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP](https://mlsanity.com/paper/2601.09065/) (2026)
- [D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation](https://mlsanity.com/paper/2404.10857/) (2024)
- [Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives](https://mlsanity.com/paper/2508.02853/) (2025)
- [Re-examining Sexism and Misogyny Classification with Annotator Attitudes](https://mlsanity.com/paper/2410.03543/) (2024)
- [Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensiveness](https://mlsanity.com/paper/2411.08977/) (2024)
- [NUTMEG: Separating Signal From Noise in Annotator Disagreement](https://mlsanity.com/paper/2507.18890/) (2025)
- [Evaluating how LLM annotations represent diverse views on contentious topics](https://mlsanity.com/paper/2503.23243/) (2025)
- [Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!](https://mlsanity.com/paper/2311.03153/) (2023)
- [Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?](https://mlsanity.com/paper/2401.12492/) (2024)
- [From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP](https://mlsanity.com/paper/2601.07988/) (2026)
- [Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias](https://mlsanity.com/paper/2601.18486/) (2026)
- [LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human-LLM Judgment Gaps](https://mlsanity.com/paper/2604.27345/) (2026)
- [Let Guidelines Guide You: A Prescriptive Guideline-Centered Data Annotation Methodology](https://mlsanity.com/paper/2406.14099/) (2024)
- [Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree](https://mlsanity.com/paper/2410.12217/) (2024)
- [SubData: Bridging Heterogeneous Datasets to Enable Theory-Driven Evaluation of Political and Demographic Perspectives in LLMs](https://mlsanity.com/paper/2412.16783/) (2024)
- [Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments](https://mlsanity.com/paper/2509.06704/) (2025)
- [Designing NLP Systems That Adapt to Diverse Worldviews](https://mlsanity.com/paper/2405.11197/) (2024)

## Abstract (arXiv)

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.