On the Interplay between Human Label Variation and Model Fairness
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Abstract (arXiv)
The impact of human label variation (HLV) on model fairness is an unexplored topic. This paper examines the interplay by comparing training on majority-vote labels with a range of HLV methods. Our experiments show that without explicit debiasing, HLV training methods have a positive impact on fairness under certain configurations.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 14 Oct 2025.
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References · 11
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 380
- 2019Human uncertainty makes classification more robustcited by 235
- 2021Toward a Perspectivist Turn in Ground Truthing for Predictive Computingcited by 130
- 2022Stop Measuring Calibration When Humans Disagreecited by 71
- 2023SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)cited by 52
- 2024VariErr NLI: Separating Annotation Error from Human Label Variationcited by 42
- 2022Fairness and Explainability: Bridging the Gap Towards Fair Model Explanationscited by 22
- 2023Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPcited by 11
- 2025Training and Evaluating with Human Label Variation: An Empirical Studycited by 8
- 2023On the Interplay between Fairness and Explainabilitycited by 7
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