Aviation Equity through Digital Scheduling Transparency: Transforming Pilot Experience through Explainable AI

Abstract

This article examines a transformative initiative implementing machine learning-backed transparency in aviation pilot scheduling systems. By addressing the traditionally opaque nature of scheduling decisions, the project fundamentally altered the relationship between flight crews and administrative processes. The initiative exposed the underlying logic of trade approval decisions, translating complex algorithmic determinations into comprehensible explanations at the intersection of explainable artificial intelligence (XAI) and procedural justice theory. Through a multi-phase implementation approach involving cross-functional collaboration, interpretable scoring models, and intuitive user interfaces, the system provided pilots with meaningful insights into both approved and rejected trade requests. This transparency not only demystified the scheduling process but also significantly reduced helpdesk queries, increased trust in the system, enabled more strategic trade requests, and transformed a source of friction into an opportunity for organizational learning. The article demonstrates how technological transparency can simultaneously enhance operational efficiency and foster workplace equity in high-pressure professional environments.