通过权重掩码消除性别偏差，提升痴呆语音检测的公平性
Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking
- 提出两种过滤方法，分离并移除与性别相关的模型权重
- 移除性别相关权重后，分类器去混淆，但检测性能轻微下降
- 适合关注医疗AI公平性与可解释性的研究者
深度变换模型已被用于检测患者语料中的语言异常以进行阿尔茨海默病（AD）早期筛查。尽管在AD语料上微调的预训练语言模型表现良好，但关于说话人性别对检测结果的影响研究仍较少。本文针对痴呆检测中的性别混淆问题，提出两种方法：扩展混淆过滤器和双过滤器，旨在隔离并去除与性别相关的模型权重。我们在包含认知障碍患者和健康对照者第一人称叙述的痴呆数据集上评估这些方法。结果表明，变换模型容易过拟合训练数据分布。破坏与性别相关的权重后，可实现去混淆的痴呆分类器，代价是检测性能略有下降。
原文摘要 · Abstract (English)
Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models (LMs) fine-tuned on AD transcripts perform well, little research has explored the effects of the gender of the speakers represented by these transcripts. This work addresses gender confounding in dementia detection and proposes two methods: the $\textit{Extended Confounding Filter}$ and the $\textit{Dual Filter}$, which isolate and ablate weights associated with gender. We evaluate these methods on dementia datasets with first-person narratives from patients with cognitive impairment and healthy controls. Our results show transformer models tend to overfit to training data distributions. Disrupting gender-related weights results in a deconfounded dementia classifier, with the trade-off of slightly reduced dementia detection performance.
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