Debiasing LLMs by Masking Unfairness-Driving Attention Heads

ACL ARR 2026 January Submission9502 Authors

Keywords: Fairness, Bias, Attention heads

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 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 part of LLM and improve measured unfairness by 534.5%-391.9% in both one-turn 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 that identifies bias heads through differential activation analysis between DA and CoT, and selectively masks only
those heads.
DiffHeads reduces unfairness by 49.4%, and 40.3% under DA and CoT, respectively, without harming model utility.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: Computational Social Science, Cultural Analytics, and NLP for Social Good;Ethics, Bias, and Fairness

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 9502