@inproceedings{kurniawan-etal-2026-interplay,
    title = "On the Interplay between Human Label Variation and Model Fairness",
    author = "Kurniawan, Kemal  and
      Mistica, Meladel  and
      Baldwin, Timothy  and
      Lau, Jey Han",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.50/",
    pages = "967--976",
    ISBN = "979-8-89176-386-9",
    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."
}