针对飞行员训练学员的疲劳模型中的性别偏见，提出改进方法并验证有效。
Toward Mitigating Sex Bias in Pilot Trainees' Stress and Fatigue Modeling
- 用决策树建模并引入公平性约束优化，减少性别偏差。
- 性别偏见降低88.31%（群体平等差），54.26%（机会均等差）。
- 适合关注航空安全与算法公平性的研究者和从业者。
尽管研究人员一直在探索飞行员，尤其是飞行员训练学员的压力与疲劳，并开发自动化检测模型，但这些模型常忽视性别等偏差。在航空这类性别分布严重失衡的关键行业中，消除偏见对实现公平且安全的预测至关重要。本研究调查了69名大学生（其中40名为飞行员训练学员，约63%为男性），先构建无偏见缓解的决策树模型，再通过阈值优化器结合人口均等性和机会均等性约束进行30次随机实例测试。使用偏见缓解后，群体平等差异下降88.31%，机会均等差异下降54.26%，且差异具有统计显著性。
原文摘要 · Abstract (English)
While researchers have been trying to understand the stress and fatigue among pilots, especially pilot trainees, and to develop stress/fatigue models to automate the process of detecting stress/fatigue, they often do not consider biases such as sex in those models. However, in a critical profession like aviation, where the demographic distribution is disproportionately skewed to one sex, it is urgent to mitigate biases for fair and safe model predictions. In this work, we investigate the perceived stress/fatigue of 69 college students, including 40 pilot trainees with around 63% male. We construct models with decision trees first without bias mitigation and then with bias mitigation using a threshold optimizer with demographic parity and equalized odds constraints 30 times with random instances. Using bias mitigation, we achieve improvements of 88.31% (demographic parity difference) and 54.26% (equalized odds difference), which are also found to be statistically significant.
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