The Role of Fatigue Risk Management Systems (FRMS) in the Implementation of Human -AI teaming in the Aviation Ecosystem.

Abstract

The emergence of Human-AI teaming within the aviation ecosystem introduces profound implications for fatigue management, operational resilience, and safety assurance. As artificial intelligence systems become increasingly embedded in flight operations, predictive analytics, crew scheduling, and cockpit decision support, the integration of Fatigue Risk Management Systems (FRMS) takes on an expanded and critical role. This paper examines how FRMS principles can support, and must be adapted to support, the safe and effective implementation of human-AI teaming, ensuring that technological innovation enhances rather than undermines human performance in safety-critical environments. The paper situates fatigue as a persistent human factor risk that continues to shape error pathways, cognitive performance, and decision-making under operational stress. AI-enabled systems hold the potential to augment fatigue management by providing real-time physiological monitoring, predictive fatigue modelling, adaptive workload distribution, and decision-support cues during periods of reduced alertness. However, the introduction of AI also reshapes the operational landscape: as tasks shift between human and machine, the cognitive workload profile of pilots may fluctuate unpredictably. In highly automated or single-pilot contexts, AI systems may inadvertently increase fatigue risks by imposing vigilance demands, imposing excessive monitoring burdens, or providing poorly calibrated levels of assistance. These emerging risks underscore the need for FRMS frameworks capable of recognising and governing the unique human-machine interactions associated with advanced automation. The paper examines how each core component of FRMS, policy, risk assessment, data-driven monitoring, and training, contributes to the governance of human-AI teaming. FRMS Policy must explicitly acknowledge AI as both a potential fatigue mitigator and a fatigue hazard, emphasising a human-centric philosophy that prioritises pilot wellbeing alongside operational efficiency. Fatigue Risk Assessment processes must incorporate new hazard categories, including over-reliance on algorithmic alerts, cognitive underload resulting from task redistribution, and increased monitoring pressures associated with supervising autonomous functions. Safety Assurance within FRMS must transform into a continuous, adaptive process capable of monitoring AI performance, evaluating AI-human workload balance, and detecting early signs of fatigue-induced system drift. The paper proposes an integrated FRMS-AI governance model tailored to the future of human-AI teaming. This model incorporates predictive fatigue analytics, adaptive automation strategies, human-machine workload harmonisation, and AI-specific fatigue indicators within FRMS oversight. The findings emphasise that the success of human-AI teaming in aviation will depend not solely on technological progress but on the robustness of FRMS to manage evolving human cognitive vulnerabilities in increasingly automated operational ecosystems.

Keywords: Fatigue Risk Management Systems (FRMS), human–AI Teaming, Aviation Fatigue, Adaptive Automation, Cognitive Workload, Predictive Analytics

DOI: 10.54941/ahfe1007828