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On the Interplay between Human Label Variation and Model Fairness
Kurniawan, Kemal Mistica, Meladel Baldwin, Timothy Lau, Jey Han
Kurniawan, Kemal
Mistica, Meladel
Baldwin, Timothy
Lau, Jey Han
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Natural Language Processing
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Conference proceeding
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http://creativecommons.org/licenses/by/4.0/
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Abstract
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.
Citation
K. Kurniawan, M. Mistica, T. Baldwin, J.H. Lau, "On the Interplay between Human Label Variation and Model Fairness," 2026, pp. 967-976.
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Findings of the Association for Computational Linguistics: EACL 2026
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Findings of the Association for Computational Linguistics: EACL 2026
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Association for Computational Linguistics