The United States Air Force (USAF) continues to be plagued by a chronic pilot shortage, one that could be exacerbated by an accompanying shortfall in the commercial airlines. As a result, efforts have increased to alleviate this shortage by finding methods to reduce pilot training attrition. We contribute to these efforts by setting forth a decision support system (DSS) for pilot candidate selection using modern machine learning techniques. In view of the recent Responsible Artificial Intelligence Strategy published by the United States Department of Defense, this research leverages interpretable and explainable machine learning methods to create traceable and equitable models that may be responsibly and reliably governed. These models are used to regress candidates’ average merit assignment selection system scores based on information available for selection and prior to training. More specifically, using data provided by the USAF from 2010 to 2018, this paper develops and analyzes multiple interpretable models based on Gaussian Bayesian networks, as well as multiple black-box models rendered explainable by SHAP values and conformal prediction. A preferred pair of interpretable and explainable models is selected and embedded within a DSS for USAF pilot candidate selection boards: the Air Force Pilot Applicant Selection System. The utilization of this DSS is explored, the analyses it enables are discussed, and relevant USAF policymaking issues are examined.

中文翻译：

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负责美国空军飞行员候选人选择的机器学习

美国空军（USAF）继续受到长期飞行员短缺的困扰，商业航空公司的飞行员短缺可能会加剧这一问题。因此，人们加大了努力，通过寻找减少飞行员培训损耗的方法来缓解这种短缺。我们通过使用现代机器学习技术制定用于飞行员候选人选择的决策支持系统（DSS）来为这些努力做出贡献。鉴于美国国防部最近发布的《负责任的人工智能战略》，这项研究利用可解释和可解释的机器学习方法来创建可追溯和公平的模型，并可以对其进行负责任和可靠的治理。这些模型用于根据可用于选择和培训之前的信息来回归候选人的平均绩效分配选择系统分数。更具体地说，本文利用美国空军 2010 年至 2018 年提供的数据，开发并分析了基于高斯贝叶斯网络的多个可解释模型，以及通过 SHAP 值和保形预测呈现可解释的多个黑盒模型。选择一对首选的可解释和可解释的模型并将其嵌入到美国空军飞行员候选人选拔委员会的 DSS 中：空军飞行员申请人选拔系统。探讨了该 DSS 的使用，讨论了它所支持的分析，并研究了相关的美国空军决策问题。