The study examined the use of explainable artificial intelligence (XAI) methods for unmanned aerial vehicles (UAVs). The aim of the study is to make the artificial intelligence decision processes in the autonomous systems of UAVs more understandable and transparent. This requirement has gained importance as UAVs are used in increasingly complex tasks. In order to increase the reliability of UAVs especially in areas such as security, military operations and agriculture, and to reinforce users' trust in these systems, the integration of XAI methods has become critical.

In the study, both model-based and post-hoc explanation methods were used. Model-based methods such as decision trees and explainable neural networks were preferred to ensure transparency of UAVs' decision-making processes. Post-hoc methods such as Local Intrepretable Model - Adnogtics Explanations (LIME) and SHapley Additive Explanations (SHAP) were used to increase the explainability of deep learning models . In the study, these techniques were evaluated to understand why UAVs make certain decisions in their complex operational processes.

As a result of the study, it was observed that the integration of Explainable Artificial Intelligence (XAI) methods into UAV systems made decision processes more transparent and thus increased the reliability of UAVs. It was emphasized that this transparency, especially in terms of public safety and ethical practices, plays a critical role in expanding the use of UAVs and making them safer.