Abstract The increasing reliance on Unmanned Aerial Vehicles (UAVs) across critical industries—including defense, logistics, and infrastructure inspection—demands robust and accurate fault diagnosis systems to ensure operational safety and efficiency. However, the integration of Artificial Intelligence (AI) in UAV fault detection and predictive maintenance raises significant legal and regulatory concerns, particularly regarding liability, accountability, and transparency. In this study, it is aimed to give a better understanding of the co-founding domains of Explainable AI (XAI) and legal framework in addressing the issues of fault diagnosis of autonomous UAV systems. It investigates the legal conflicts that may arise from aviation safety compliance regarding the reliability of black-box-like AI models used for the detection of drone faults, and the study argues why interpretable AI is a must-have for compliance with regulatory authorities and courtroom verdicts. The liability attribution in UAV failures is further discussed to assess whether responsibility lies with manufacturers, software developers, or end-users in cases of AI-induced malfunctions. By examining current aviation safety laws, data protection policies, and ethical AI guidelines, the work proposes a framework that integrates transparent AI methodologies to ensure legal compliance while enhancing UAV reliability. The findings highlight that XAI-driven fault diagnosis improves safety and maintenance protocols while playing a crucial role in mitigating perhaps legal risks and fostering supposedly trust in AI-powered UAV operations.
Fadhil et al. (2025) studied this question.