Summary of Toward Mitigating Sex Bias in Pilot Trainees’ Stress and Fatigue Modeling, by Rachel Pfeifer et al.
Toward Mitigating Sex Bias in Pilot Trainees’ Stress and Fatigue Modeling
by Rachel Pfeifer, Sudip Vhaduri, Mark Wilson, Julius Keller
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study addresses the issue of biases in stress and fatigue models used for pilot trainees, with a focus on mitigating sex-based biases. The authors investigate the perceived stress and fatigue levels of 69 college students, including 40 pilot trainees (63% male). They construct decision tree models without bias mitigation and then using threshold optimizers with demographic parity and equalized odds constraints. The results show significant improvements in model performance after applying bias mitigation techniques, with increases of 88.31% for demographic parity difference and 54.26% for equalized odds difference. This research is relevant to the development of fair and safe models for detecting stress and fatigue among pilots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make computer programs better at understanding when people are stressed or tired, especially pilots in training. The problem is that these programs often don’t consider whether the person using them is a man or woman. Since most pilots are men, this can lead to unfair results. The researchers asked 69 college students, including 40 pilot trainees, about their stress and fatigue levels. They then tested different ways of making the program fairer. The results showed that by making the program more fair, they could get better answers, which is important for keeping pilots safe. |
Keywords
» Artificial intelligence » Decision tree
