@inproceedings{han-etal-2026-debiasing,
    title = "Debiasing {LLM}s by Masking Unfairness-Driving Attention Heads",
    author = "Han, Tingxu  and
      Song, Wei  and
      Ding, Ziqi  and
      Li, Ziming  and
      Fang, Chunrong  and
      Li, Yuekang  and
      Liu, Dongfang  and
      Chen, Zhenyu  and
      Wang, Zhenting",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1893/",
    pages = "37980--37992",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) increasingly mediate decisions in domains where unfair treatment of demographic groups is unacceptable. Existing work probes when biased outputs appear, but gives little insight into the mechanisms that generate them, leaving existing mitigations largely fragile. In this paper, we conduct a systematic investigation of LLM unfairness and propose DiffHeads{---}a lightweight debiasing framework for LLMs. We first compare Direct-Answer (DA) prompting to Chain-of-Thought (CoT) prompting across eight representative open- and closed-source LLMs. DA will trigger the nature-bias component of the LLM and reduce measured unfairness by 391.9{\%}- 534.5{\%} in both one- and two-turn dialogues. Next, we define a token-to-head contribution score that traces each token{'}s influence back to individual attention heads. This reveals a small cluster of bias heads that activate under DA but stay largely dormant with CoT, providing the first causal link between prompting strategy and bias emergence. Finally, building on this insight, we propose DiffHeads, which identify bias heads through differential activation analysis between DA and CoT and selectively mask only those heads. DiffHeads reduces unfairness by 49.4{\%}, and 40.3{\%} under DA and CoT, respectively, without harming model utility."
}