Explainable AI (XAI) for FAA Certifiable Aviation Systems

Abstract

Artificial Intelligence (AI) has emerged as a crucial technology in the field of aviation, presenting promising opportunities for enhancing operational efficiency, autonomous decision-making, predictive maintenance, and flight safety. But the use of AI in safety-critical applications in the aerospace industry is still hindered by the opacity and interpretability of complex machine learning algorithms. The traditional black-box approach to AI systems is problematic for the certification of safety and reliability that the Federal Aviation Administration (FAA) demands, especially when it comes to traceability, accountability, verification, and human trust. In this study, an Explainable Artificial Intelligence (XAI) framework specifically for FAA-certifiable aviation systems is proposed. The framework incorporates interpretable machine learning methods, safety assurance protocols, human oversight elements, and compliance-driven validation procedures, all aimed at boosting transparency in AI-powered aviation operations. The research examines the applicability of key XAI methods, such as SHAP, LIME, and rule-based XAI models, in aviation safety scenarios. A simulation-based methodology is used for the evaluation of explainability performance, certification readiness, trustworthiness, and operational reliability in various aviation AI scenarios. The suggested framework shows how explainability mechanisms can enhance human comprehension of AI decisions, enable safety verification procedures, and boost regulatory compliance capabilities for autonomous aviation systems. The results also suggest that XAI can be incorporated into aviation AI systems to facilitate the safe implementation of intelligent aerospace systems and mitigate certification challenges of black-box AI systems. This study offers a structured approach for an FAA-oriented XAI certification framework and offers practical recommendations for the development of transparent, trustworthy, and certifiable AI-based aviation systems.