解决谣言检测中的不公平问题，提升模型性能与公平性
Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue
- 通过两步框架识别干扰敏感属性并学习不变表示
- 在不依赖敏感属性标注下显著提升检测准确率与公平性
- 可无缝集成现有检测器，适合注重公平性的应用
谣言检测中因混淆的敏感属性导致的性能下降和群体不公平问题尚未得到充分研究。为此，我们提出一种两步框架：首先识别影响检测性能并引发群体不公平的混淆敏感属性；随后通过不变学习获取等信息量的表示。该方法无需敏感属性标注即可处理多种群体组合。实验表明，本方法可轻松集成至现有谣言检测器中，在显著提升检测性能的同时有效改善公平性。
原文摘要 · Abstract (English)
The degraded performance and group unfairness caused by confounding sensitive attributes in rumor detection remains relatively unexplored. To address this, we propose a two-step framework. Initially, it identifies confounding sensitive attributes that limit rumor detection performance and cause unfairness across groups. Subsequently, we aim to learn equally informative representations through invariant learning. Our method considers diverse sets of groups without sensitive attribute annotations. Experiments show our method easily integrates with existing rumor detectors, significantly improving both their detection performance and fairness.
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