On the Interplay between Human Label Variation and Model Fairness
arXiv:2510.12036
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.
10 pages, 7 figures. Accepted to EACL Findings 2026