Toward Mitigating Sex Bias in Pilot Trainees' Stress and Fatigue Modeling
This addresses bias mitigation for fair and safe predictions in aviation, a critical domain with skewed demographics, but is incremental as it applies existing bias mitigation methods to a specific dataset.
The study tackled sex bias in stress and fatigue modeling for pilot trainees by investigating perceived stress/fatigue in 69 college students, including 40 pilot trainees, and achieved improvements of 88.31% in demographic parity and 54.26% in equalized odds using bias mitigation techniques.
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.