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.

翻译：人类标注差异（HLV）对模型公平性的影响是一个尚未被探索的课题。本文通过比较基于多数投票标签的训练与一系列HLV方法的训练，来研究二者之间的相互作用。我们的实验表明，在没有显式去偏的情况下，HLV训练方法在某些配置下对公平性具有积极影响。